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run_linear_scamp.py
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import argparse
from data_utils import dataset_classes, utils
from datetime import datetime
from training_utils import train_utils, quantization
from model_utils import get_model, cnn, rnn
import json
import model_utils
import numpy as np
import os
import random
import sys
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader, Subset
from torch import optim
from torch.optim.lr_scheduler import CosineAnnealingLR, ReduceLROnPlateau
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
bit_conversion = {
'binary':1,
'laurie_insp_binary': 1,
'tanh_x':1,
'tanh_mx':1,
'suyeon_gumbel_binary':1,
'laurie_ternary':1.5,
'suyeon_gumbel_ternary':1.5,
'full': 32,
}
# default size is 64. If something different, add to dict
resize_dict = {
"analognet1" : 256,
"analognet2" : 28,
"liu_2020": 32,
"lenet5" : 32,
"laurie_cnn_2": 32,
"group_so_2022": 256,
"FULL_linear": 256,
"event_cam": 64,
'so_2022_new':256,
'so_2022_batch_norm': 256,
'CNN_bin_scamp_256':256
}
video_datasets = ['cambridge', 'hmdb51', 'jester']
image_datasets = ['mnist', 'cifar10']
paths_datasets = {
'cambridge': "/media/data4b/haleyso/Cambridge_Hand_Gesture",
"hmdb51": "/media/data4b/haleyso/human_motion_data/hmdb51_jpg",
"jester": "/media/data4b/haleyso/Jester/20bn-jester-v1"
}
cnn_names = sorted(name for name in cnn.__dict__
if callable(cnn.__dict__[name]))
rnn_names = sorted(name for name in rnn.__dict__
if callable(rnn.__dict__[name]))
def test_scamp_linear(config, scamp_data_path, torch_data_path, debug):
if 'seed' in config.keys():
seed = config['seed']
print(f'seed: {seed}')
else:
print("default seed is 2023")
seed = 2023
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
cnn_model_type = config['model']['cnn_type']
rnn_model_type = config['model']['rnn_type']
cnn_params = config['model']['cnn_params']
rnn_params = config['model']['rnn_params']
train_params = config['train_params']
batch_size = config['batch_size']
max_pool = config['max_pool']
timesteps = config['timesteps']
task = config["task"]
# h, w = 224, 224
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
dataset = (config['dataset']).lower()
num_classes = 9
# resize shape
if cnn_model_type in resize_dict.keys():
resize_shape = resize_dict[cnn_model_type]
else:
resize_shape = 64
print(resize_shape)
# models
# cnn model
cnn_parameters= {
"cnn_model_type": config['model']['cnn_type'],
"dr_rate": cnn_params['dr_rate'],
"kernel_size": cnn_params['kernel_size'],
"stride": cnn_params['stride'],
"dilation": cnn_params['dilation'],
"groups": cnn_params['groups'],
"bias": cnn_params['bias'],
"cnn_method": train_params["cnn_method"],
"final_conv_method": train_params["final_conv_method"]
}
# rnn model
rnn_parameters = {
"rnn_model_type": config['model']['rnn_type'],
"kernel_size": rnn_params['kernel_size'],
"stride": rnn_params['stride'],
"dilation": rnn_params['dilation'],
"groups": rnn_params['groups'],
"bias": rnn_params['bias'],
"rnn_num_layers": rnn_params['rnn_num_layers'],
"rnn_hidden_size": rnn_params['rnn_hidden_size'],
"method": train_params["rnn_method"]
}
# patches... adding things to the json file
if ("cnn_output_quantization" in train_params.keys()):
cnn_parameters["cnn_output_quantization"] = train_params["cnn_output_quantization"]
# print(train_params.get("cnn_output_quantization"), type(train_params), cnn_parameters["cnn_output_quantization"])
# sys.exit()
else:
cnn_parameters["cnn_output_quantization"] = train_params["cnn_method"]
if ("hidden_quantization" in train_params.keys()):
rnn_parameters["hidden_quantization"] = train_params["hidden_quantization"]
else:
rnn_parameters["hidden_quantization"] = train_params["rnn_method"]
if ("hidden_weight_init_scale" in rnn_params.keys()):
rnn_parameters["hidden_weight_init_scale"] = rnn_params["hidden_weight_init_scale"]
else:
rnn_parameters["hidden_weight_init_scale"] = 1
if ("forget_weight_init_scale" in rnn_params.keys()):
rnn_parameters["forget_weight_init_scale"] = rnn_params["forget_weight_init_scale"]
else:
rnn_parameters["forget_weight_init_scale"] = 1
if ("out_weight_init_scale" in rnn_params.keys()):
rnn_parameters["out_weight_init_scale"] = rnn_params["out_weight_init_scale"]
else:
rnn_parameters["out_weight_init_scale"] = 1
if ("r_weight_init_scale" in rnn_params.keys()):
rnn_parameters["r_weight_init_scale"] = rnn_params["r_weight_init_scale"]
else:
rnn_parameters["r_weight_init_scale"] = 1
if ("z_weight_init_scale" in rnn_params.keys()):
rnn_parameters["z_weight_init_scale"] = rnn_params["z_weight_init_scale"]
else:
rnn_parameters["z_weight_init_scale"] = 1
if ("i_weight_init_scale" in rnn_params.keys()):
rnn_parameters["i_weight_init_scale"] = rnn_params["i_weight_init_scale"]
else:
rnn_parameters["i_weight_init_scale"] = 1
if ("gate_quantization" in train_params.keys()):
rnn_parameters["gate_quantization"] = train_params["gate_quantization"]
else:
rnn_parameters["gate_quantization"] = "full"
# methods for quantization:
final_conv_method = quantization.get_method_func(config['train_params']['final_conv_method'])
linear_method = quantization.get_method_func(config['train_params']['linear_method'])
# print("cnn method ",cnn_method)
model = get_model.get_model(config, cnn_model_type, cnn_parameters, rnn_model_type, rnn_parameters, dataset, num_classes, max_pool)
model = model.cuda()
path2weights = config["path2weights"]
checkpoint = torch.load(path2weights)
model.load_state_dict(checkpoint)
# torch_test_ids = []
# torch_test_labels = []
# classes = os.listdir(torch_data_path)
# classes.sort()
# for cls in range(len(classes)):
# class_folder = int(cls)+1
# class_path = os.path.join(torch_data_path, str(class_folder))
# videos = os.listdir(class_path)
# videos.sort()
# for vid in videos:
# vid_path = os.path.join(class_path, vid, "15", "6_ft_ot.BMP")
# torch_test_ids.append(vid_path)
# torch_test_labels.append(int(cls))
# scamp_test_ids = []
# scamp_test_labels = []
# classes = os.listdir(scamp_data_path)
# classes.sort()
# for cls in range(len(classes)):
# class_path = os.path.join(scamp_data_path, str(cls))
# videos = os.listdir(class_path)
# videos.sort()
# for vid in videos:
# vid_path = os.path.join(class_path, vid,"15_output.BMP")
# scamp_test_ids.append(vid_path)
# scamp_test_labels.append(int(cls))
# torch_ds = dataset_classes.ScampDataset(ids= torch_test_ids, labels= torch_test_labels, input_white=2.0, pytorch_size=True)
# torch_dl = DataLoader(torch_ds, batch_size=1, num_workers=1, shuffle=False)
# scamp_ds = dataset_classes.ScampDataset(ids= scamp_test_ids, labels= scamp_test_labels, input_white=5.0)
# scamp_dl = DataLoader(scamp_ds, batch_size=1, num_workers=1, shuffle=False)
train_set_names = ['Set1_1_0001', 'Set1_1_0003', 'Set1_1_0004', 'Set1_1_0005', 'Set1_1_0006', 'Set1_1_0007', 'Set1_1_0008', 'Set1_1_0009', 'Set1_1_0010', 'Set1_1_0011', 'Set1_1_0012', 'Set1_1_0016', 'Set1_1_0017', 'Set1_1_0018', 'Set2_1_0000', 'Set2_1_0001', 'Set2_1_0002', 'Set2_1_0003', 'Set2_1_0004', 'Set2_1_0006', 'Set2_1_0007', 'Set2_1_0009', 'Set2_1_0010', 'Set2_1_0011', 'Set2_1_0012', 'Set2_1_0013', 'Set2_1_0014', 'Set2_1_0015', 'Set2_1_0016', 'Set2_1_0017', 'Set2_1_0018', 'Set2_1_0019', 'Set3_1_0000', 'Set3_1_0001', 'Set3_1_0002', 'Set3_1_0003', 'Set3_1_0004', 'Set3_1_0005', 'Set3_1_0009', 'Set3_1_0010', 'Set3_1_0011', 'Set3_1_0012', 'Set3_1_0014', 'Set3_1_0015', 'Set3_1_0016', 'Set3_1_0018', 'Set3_1_0019', 'Set4_1_0000', 'Set4_1_0001', 'Set4_1_0002', 'Set4_1_0003', 'Set4_1_0004', 'Set4_1_0005', 'Set4_1_0006', 'Set4_1_0007', 'Set4_1_0010', 'Set4_1_0011', 'Set4_1_0012', 'Set4_1_0013', 'Set4_1_0014', 'Set4_1_0015', 'Set4_1_0016', 'Set4_1_0017', 'Set4_1_0018', 'Set4_1_0019', 'Set5_1_0003', 'Set5_1_0004', 'Set5_1_0007', 'Set5_1_0009', 'Set5_1_0010', 'Set5_1_0011', 'Set5_1_0012', 'Set5_1_0013', 'Set5_1_0014', 'Set5_1_0015', 'Set5_1_0016', 'Set5_1_0017', 'Set5_1_0018', 'Set5_1_0019', 'Set1_1_0000', 'Set1_2_0001', 'Set1_2_0002', 'Set1_2_0003', 'Set1_2_0005', 'Set1_2_0007', 'Set1_2_0008', 'Set1_2_0009', 'Set1_2_0010', 'Set1_2_0011', 'Set1_2_0012', 'Set1_2_0013', 'Set1_2_0014', 'Set1_2_0015', 'Set1_2_0016', 'Set1_2_0019', 'Set2_2_0000', 'Set2_2_0001', 'Set2_2_0002', 'Set2_2_0003', 'Set2_2_0006', 'Set2_2_0007', 'Set2_2_0008', 'Set2_2_0009', 'Set2_2_0010', 'Set2_2_0011', 'Set2_2_0012', 'Set2_2_0013', 'Set2_2_0015', 'Set2_2_0016', 'Set2_2_0017', 'Set2_2_0018', 'Set2_2_0019', 'Set3_2_0000', 'Set3_2_0001', 'Set3_2_0003', 'Set3_2_0004', 'Set3_2_0006', 'Set3_2_0010', 'Set3_2_0014', 'Set3_2_0015', 'Set3_2_0016', 'Set3_2_0017', 'Set3_2_0018', 'Set3_2_0019', 'Set4_2_0000', 'Set4_2_0001', 'Set4_2_0002', 'Set4_2_0003', 'Set4_2_0004', 'Set4_2_0005', 'Set4_2_0006', 'Set4_2_0007', 'Set4_2_0008', 'Set4_2_0010', 'Set4_2_0011', 'Set4_2_0012', 'Set4_2_0013', 'Set4_2_0014', 'Set4_2_0016', 'Set4_2_0017', 'Set4_2_0018', 'Set4_2_0019', 'Set5_2_0000', 'Set5_2_0001', 'Set5_2_0002', 'Set5_2_0003', 'Set5_2_0004', 'Set5_2_0005', 'Set5_2_0006', 'Set5_2_0008', 'Set5_2_0009', 'Set5_2_0010', 'Set5_2_0012', 'Set5_2_0013', 'Set5_2_0014', 'Set5_2_0015', 'Set5_2_0016', 'Set5_2_0017', 'Set5_2_0018', 'Set1_2_0000', 'Set1_3_0001', 'Set1_3_0002', 'Set1_3_0003', 'Set1_3_0005', 'Set1_3_0006', 'Set1_3_0007', 'Set1_3_0008', 'Set1_3_0009', 'Set1_3_0010', 'Set1_3_0011', 'Set1_3_0012', 'Set1_3_0013', 'Set1_3_0014', 'Set1_3_0015', 'Set1_3_0017', 'Set1_3_0018', 'Set1_3_0019', 'Set2_3_0000', 'Set2_3_0004', 'Set2_3_0006', 'Set2_3_0007', 'Set2_3_0008', 'Set2_3_0010', 'Set2_3_0012', 'Set2_3_0013', 'Set2_3_0015', 'Set2_3_0016', 'Set2_3_0017', 'Set2_3_0018', 'Set2_3_0019', 'Set3_3_0000', 'Set3_3_0001', 'Set3_3_0002', 'Set3_3_0003', 'Set3_3_0005', 'Set3_3_0006', 'Set3_3_0008', 'Set3_3_0009', 'Set3_3_0010', 'Set3_3_0012', 'Set3_3_0013', 'Set3_3_0014', 'Set3_3_0015', 'Set3_3_0016', 'Set3_3_0018', 'Set3_3_0019', 'Set4_3_0000', 'Set4_3_0001', 'Set4_3_0002', 'Set4_3_0003', 'Set4_3_0004', 'Set4_3_0005', 'Set4_3_0006', 'Set4_3_0009', 'Set4_3_0011', 'Set4_3_0012', 'Set4_3_0013', 'Set4_3_0014', 'Set4_3_0015', 'Set4_3_0016', 'Set4_3_0017', 'Set4_3_0019', 'Set5_3_0000', 'Set5_3_0001', 'Set5_3_0002', 'Set5_3_0003', 'Set5_3_0004', 'Set5_3_0005', 'Set5_3_0006', 'Set5_3_0008', 'Set5_3_0009', 'Set5_3_0010', 'Set5_3_0011', 'Set5_3_0012', 'Set5_3_0013', 'Set5_3_0014', 'Set5_3_0017', 'Set5_3_0018', 'Set5_3_0019', 'Set1_3_0000', 'Set1_4_0001', 'Set1_4_0002', 'Set1_4_0003', 'Set1_4_0004', 'Set1_4_0005', 'Set1_4_0006', 'Set1_4_0007', 'Set1_4_0009', 'Set1_4_0010', 'Set1_4_0014', 'Set1_4_0015', 'Set1_4_0016', 'Set1_4_0017', 'Set1_4_0018', 'Set1_4_0019', 'Set2_4_0000', 'Set2_4_0002', 'Set2_4_0003', 'Set2_4_0004', 'Set2_4_0005', 'Set2_4_0006', 'Set2_4_0007', 'Set2_4_0008', 'Set2_4_0009', 'Set2_4_0010', 'Set2_4_0013', 'Set2_4_0014', 'Set2_4_0015', 'Set2_4_0016', 'Set2_4_0018', 'Set2_4_0019', 'Set3_4_0000', 'Set3_4_0001', 'Set3_4_0002', 'Set3_4_0003', 'Set3_4_0005', 'Set3_4_0006', 'Set3_4_0007', 'Set3_4_0008', 'Set3_4_0009', 'Set3_4_0010', 'Set3_4_0011', 'Set3_4_0012', 'Set3_4_0013', 'Set3_4_0014', 'Set3_4_0015', 'Set3_4_0016', 'Set3_4_0017', 'Set3_4_0018', 'Set3_4_0019', 'Set4_4_0000', 'Set4_4_0001', 'Set4_4_0003', 'Set4_4_0004', 'Set4_4_0005', 'Set4_4_0006', 'Set4_4_0007', 'Set4_4_0008', 'Set4_4_0009', 'Set4_4_0010', 'Set4_4_0011', 'Set4_4_0013', 'Set4_4_0014', 'Set4_4_0015', 'Set4_4_0016', 'Set4_4_0017', 'Set4_4_0018', 'Set4_4_0019', 'Set5_4_0000', 'Set5_4_0002', 'Set5_4_0006', 'Set5_4_0009', 'Set5_4_0010', 'Set5_4_0012', 'Set5_4_0013', 'Set5_4_0015', 'Set5_4_0016', 'Set5_4_0017', 'Set5_4_0018', 'Set1_4_0000', 'Set1_5_0001', 'Set1_5_0002', 'Set1_5_0003', 'Set1_5_0004', 'Set1_5_0005', 'Set1_5_0006', 'Set1_5_0007', 'Set1_5_0009', 'Set1_5_0011', 'Set1_5_0012', 'Set1_5_0013', 'Set1_5_0014', 'Set1_5_0015', 'Set1_5_0016', 'Set1_5_0017', 'Set1_5_0018', 'Set1_5_0019', 'Set2_5_0001', 'Set2_5_0002', 'Set2_5_0003', 'Set2_5_0004', 'Set2_5_0005', 'Set2_5_0006', 'Set2_5_0007', 'Set2_5_0008', 'Set2_5_0009', 'Set2_5_0010', 'Set2_5_0011', 'Set2_5_0012', 'Set2_5_0013', 'Set2_5_0014', 'Set2_5_0015', 'Set2_5_0016', 'Set2_5_0018', 'Set2_5_0019', 'Set3_5_0000', 'Set3_5_0002', 'Set3_5_0003', 'Set3_5_0004', 'Set3_5_0006', 'Set3_5_0008', 'Set3_5_0010', 'Set3_5_0011', 'Set3_5_0013', 'Set3_5_0014', 'Set3_5_0015', 'Set3_5_0016', 'Set3_5_0017', 'Set3_5_0018', 'Set4_5_0001', 'Set4_5_0002', 'Set4_5_0004', 'Set4_5_0005', 'Set4_5_0006', 'Set4_5_0007', 'Set4_5_0009', 'Set4_5_0012', 'Set4_5_0013', 'Set4_5_0014', 'Set4_5_0015', 'Set4_5_0016', 'Set4_5_0017', 'Set4_5_0018', 'Set4_5_0019', 'Set5_5_0001', 'Set5_5_0002', 'Set5_5_0003', 'Set5_5_0004', 'Set5_5_0005', 'Set5_5_0006', 'Set5_5_0007', 'Set5_5_0010', 'Set5_5_0011', 'Set5_5_0013', 'Set5_5_0014', 'Set5_5_0015', 'Set5_5_0017', 'Set5_5_0018', 'Set5_5_0019', 'Set1_5_0000', 'Set1_6_0002', 'Set1_6_0003', 'Set1_6_0004', 'Set1_6_0005', 'Set1_6_0006', 'Set1_6_0007', 'Set1_6_0008', 'Set1_6_0010', 'Set1_6_0012', 'Set1_6_0013', 'Set1_6_0014', 'Set1_6_0016', 'Set1_6_0017', 'Set1_6_0018', 'Set1_6_0019', 'Set2_6_0001', 'Set2_6_0002', 'Set2_6_0003', 'Set2_6_0004', 'Set2_6_0005', 'Set2_6_0006', 'Set2_6_0007', 'Set2_6_0009', 'Set2_6_0010', 'Set2_6_0011', 'Set2_6_0012', 'Set2_6_0013', 'Set2_6_0014', 'Set2_6_0015', 'Set2_6_0017', 'Set2_6_0018', 'Set2_6_0019', 'Set3_6_0000', 'Set3_6_0001', 'Set3_6_0002', 'Set3_6_0003', 'Set3_6_0004', 'Set3_6_0005', 'Set3_6_0006', 'Set3_6_0007', 'Set3_6_0008', 'Set3_6_0009', 'Set3_6_0011', 'Set3_6_0012', 'Set3_6_0014', 'Set3_6_0015', 'Set3_6_0016', 'Set3_6_0017', 'Set3_6_0018', 'Set3_6_0019', 'Set4_6_0000', 'Set4_6_0001', 'Set4_6_0002', 'Set4_6_0003', 'Set4_6_0005', 'Set4_6_0007', 'Set4_6_0008', 'Set4_6_0009', 'Set4_6_0011', 'Set4_6_0012', 'Set4_6_0013', 'Set4_6_0014', 'Set4_6_0015', 'Set4_6_0016', 'Set4_6_0017', 'Set4_6_0018', 'Set4_6_0019', 'Set5_6_0000', 'Set5_6_0001', 'Set5_6_0002', 'Set5_6_0004', 'Set5_6_0009', 'Set5_6_0012', 'Set5_6_0013', 'Set5_6_0014', 'Set5_6_0015', 'Set5_6_0016', 'Set5_6_0018', 'Set5_6_0019', 'Set1_6_0001', 'Set1_7_0001', 'Set1_7_0002', 'Set1_7_0003', 'Set1_7_0005', 'Set1_7_0006', 'Set1_7_0007', 'Set1_7_0008', 'Set1_7_0009', 'Set1_7_0010', 'Set1_7_0011', 'Set1_7_0012', 'Set1_7_0013', 'Set1_7_0014', 'Set1_7_0018', 'Set1_7_0019', 'Set2_7_0001', 'Set2_7_0003', 'Set2_7_0004', 'Set2_7_0005', 'Set2_7_0007', 'Set2_7_0008', 'Set2_7_0009', 'Set2_7_0010', 'Set2_7_0011', 'Set2_7_0012', 'Set2_7_0013', 'Set2_7_0014', 'Set2_7_0015', 'Set2_7_0016', 'Set2_7_0017', 'Set2_7_0019', 'Set3_7_0001', 'Set3_7_0002', 'Set3_7_0003', 'Set3_7_0004', 'Set3_7_0005', 'Set3_7_0008', 'Set3_7_0010', 'Set3_7_0011', 'Set3_7_0012', 'Set3_7_0013', 'Set3_7_0015', 'Set3_7_0018', 'Set3_7_0019', 'Set4_7_0000', 'Set4_7_0001', 'Set4_7_0002', 'Set4_7_0003', 'Set4_7_0004', 'Set4_7_0005', 'Set4_7_0006', 'Set4_7_0007', 'Set4_7_0008', 'Set4_7_0009', 'Set4_7_0010', 'Set4_7_0011', 'Set4_7_0013', 'Set4_7_0014', 'Set4_7_0015', 'Set4_7_0016', 'Set4_7_0017', 'Set4_7_0018', 'Set4_7_0019', 'Set5_7_0000', 'Set5_7_0003', 'Set5_7_0004', 'Set5_7_0005', 'Set5_7_0006', 'Set5_7_0007', 'Set5_7_0008', 'Set5_7_0009', 'Set5_7_0010', 'Set5_7_0012', 'Set5_7_0013', 'Set5_7_0015', 'Set5_7_0016', 'Set5_7_0017', 'Set5_7_0018', 'Set5_7_0019', 'Set1_7_0000', 'Set1_8_0002', 'Set1_8_0004', 'Set1_8_0005', 'Set1_8_0006', 'Set1_8_0007', 'Set1_8_0008', 'Set1_8_0011', 'Set1_8_0013', 'Set1_8_0014', 'Set1_8_0015', 'Set1_8_0016', 'Set1_8_0018', 'Set1_8_0019', 'Set2_8_0000', 'Set2_8_0001', 'Set2_8_0003', 'Set2_8_0004', 'Set2_8_0005', 'Set2_8_0006', 'Set2_8_0007', 'Set2_8_0008', 'Set2_8_0009', 'Set2_8_0010', 'Set2_8_0011', 'Set2_8_0012', 'Set2_8_0013', 'Set2_8_0014', 'Set2_8_0015', 'Set2_8_0016', 'Set2_8_0017', 'Set2_8_0018', 'Set2_8_0019', 'Set3_8_0000', 'Set3_8_0004', 'Set3_8_0005', 'Set3_8_0006', 'Set3_8_0007', 'Set3_8_0008', 'Set3_8_0010', 'Set3_8_0011', 'Set3_8_0012', 'Set3_8_0014', 'Set3_8_0015', 'Set3_8_0016', 'Set3_8_0018', 'Set3_8_0019', 'Set4_8_0000', 'Set4_8_0001', 'Set4_8_0002', 'Set4_8_0003', 'Set4_8_0005', 'Set4_8_0007', 'Set4_8_0008', 'Set4_8_0009', 'Set4_8_0010', 'Set4_8_0011', 'Set4_8_0012', 'Set4_8_0013', 'Set4_8_0014', 'Set4_8_0015', 'Set4_8_0016', 'Set4_8_0017', 'Set4_8_0018', 'Set5_8_0001', 'Set5_8_0002', 'Set5_8_0003', 'Set5_8_0004', 'Set5_8_0005', 'Set5_8_0007', 'Set5_8_0008', 'Set5_8_0009', 'Set5_8_0010', 'Set5_8_0011', 'Set5_8_0012', 'Set5_8_0014', 'Set5_8_0015', 'Set5_8_0016', 'Set5_8_0017', 'Set5_8_0019', 'Set1_8_0000', 'Set1_9_0001', 'Set1_9_0003', 'Set1_9_0004', 'Set1_9_0006', 'Set1_9_0007', 'Set1_9_0009', 'Set1_9_0010', 'Set1_9_0011', 'Set1_9_0012', 'Set1_9_0013', 'Set1_9_0014', 'Set1_9_0015', 'Set1_9_0016', 'Set1_9_0017', 'Set1_9_0018', 'Set1_9_0019', 'Set2_9_0000', 'Set2_9_0001', 'Set2_9_0002', 'Set2_9_0003', 'Set2_9_0004', 'Set2_9_0005', 'Set2_9_0006', 'Set2_9_0010', 'Set2_9_0011', 'Set2_9_0012', 'Set2_9_0013', 'Set2_9_0016', 'Set2_9_0017', 'Set2_9_0018', 'Set2_9_0019', 'Set3_9_0001', 'Set3_9_0002', 'Set3_9_0003', 'Set3_9_0005', 'Set3_9_0006', 'Set3_9_0007', 'Set3_9_0010', 'Set3_9_0011', 'Set3_9_0012', 'Set3_9_0013', 'Set3_9_0014', 'Set3_9_0015', 'Set3_9_0017', 'Set3_9_0018', 'Set4_9_0000', 'Set4_9_0001', 'Set4_9_0002', 'Set4_9_0003', 'Set4_9_0004', 'Set4_9_0005', 'Set4_9_0007', 'Set4_9_0008', 'Set4_9_0009', 'Set4_9_0012', 'Set4_9_0013', 'Set4_9_0014', 'Set4_9_0015', 'Set4_9_0016', 'Set4_9_0017', 'Set4_9_0018', 'Set4_9_0019', 'Set5_9_0000', 'Set5_9_0001', 'Set5_9_0002', 'Set5_9_0003', 'Set5_9_0004', 'Set5_9_0007', 'Set5_9_0008', 'Set5_9_0009', 'Set5_9_0010', 'Set5_9_0011', 'Set5_9_0012', 'Set5_9_0013', 'Set5_9_0014', 'Set5_9_0016', 'Set5_9_0017', 'Set5_9_0018', 'Set5_9_0019', 'Set1_9_0000']
fine_tune_ids = []
fine_tune_labels = []
test_ids = []
test_labels = []
val_ids = []
val_labels = []
fine_tune_data_path = '/media/data4b/haleyso/cambridge_all_outputs_batched/'
train_2_data_path = '/media/data4b/haleyso/cambridge_all_outputs_batched_2/'
classes = os.listdir(fine_tune_data_path)
classes.sort()
for cls in range(len(classes)):
class_path = os.path.join(fine_tune_data_path, str(cls))
videos = os.listdir(class_path)
videos.sort()
# print(len(videos))
for vid in videos:
vid_path = os.path.join(class_path, vid, "15_output.BMP")
if os.path.exists(vid_path):
if vid in train_set_names:
fine_tune_ids.append(vid_path)
fine_tune_labels.append(int(cls))
val_ids.append(os.path.join(train_2_data_path, str(cls), vid, "15_output.BMP" ))
val_labels.append(int(cls))
else:
test_ids.append(vid_path)
test_labels.append(int(cls))
scamp_ds = dataset_classes.ScampDataset(ids= test_ids, labels= test_labels, input_white=5.0)
scamp_dl = DataLoader(scamp_ds, batch_size=1, num_workers=1, shuffle=False)
dl = scamp_dl
correct = 0
total = len(dl)
for xb, yb, name in tqdm(dl):
xb = xb.cuda()
# print(xb.size())
# sys.exit()
yb = yb.cuda()
output = model(xb, linear_only=True)
pred = output.argmax(dim=1, keepdim=True)
corrects=pred.eq(yb.view_as(pred)).sum().item()
correct +=corrects
# print(pred, yb)
print(f'Accuracy: {100*correct/total:.2f}' )
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='RNN CNN- scamp linear layer tester')
parser.add_argument('-c', '--config', default=None, type=str, help='config file path (default: None)')
parser.add_argument('-sd', '--scamp_data', default='/media/data4b/haleyso/cambridge_outputs/', type=str, help='scamp file path (default: {default})')
parser.add_argument('-td', '--torch_data', default='/home/haleyso/CNN_CGRU/intermediates/', type=str, help='intermediates torch path (default: {default}})')
parser.add_argument('-d', '--debug', default=False, type=str, help='True: use pytorch inputs. False: use SCAMP inputs')
args = parser.parse_args()
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
if args.config: # load config file
with open(args.config) as handle:
config = json.load(handle)
else:
sys.exit("Add config file")
test_scamp_linear(config, args.scamp_data, args.torch_data, args.debug)