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collect_distribution.py
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collect_distribution.py
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import os, time
import h5py
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from spikingjelly.clock_driven import functional
from model import create_net
from dataset import create_dataset
from config import parser
from utils import set_seed
@torch.no_grad()
def firing_num(args):
args.model.load_state_dict(torch.load(args.modelPath))
args.model.eval()
save_name_file = os.path.join(args.name, "S_Feature")
args.path = save_name_file
if not os.path.exists(save_name_file):
os.makedirs(save_name_file)
f = h5py.File(os.path.join(save_name_file, "all.h5"), "w")
all_idx = 0
flag = True
for n_iter, (input, labels) in enumerate(args.test_loader):
functional.reset_net(args.model)
b = input.size()[0]
input = input.reshape(
b * args.clip,
input.size()[2],
input.size()[3],
input.size()[4],
input.size()[5],
)
input = input.float().to(args.device)
if len(labels.shape) == 3:
labels = labels.reshape(b * args.clip, labels.size()[2])
labels = labels.float().to(args.device)
else:
labels = labels.reshape(b * args.clip, labels.size()[2], labels.size()[3])
labels = labels[:, 1, :].float().to(args.device)
output, firing_nums, mps = args.model(input=input, mp_collect=True)
for i in range(len(firing_nums)):
if i < 3:
firing_num_mat = firing_nums[i].reshape(
b * args.clip,
-1,
firing_nums[i].size()[2],
firing_nums[i].size()[3],
firing_nums[i].size()[4],
)
# print(firing_num_mat.shape)
f.create_dataset(
str(all_idx) + "_S_" + str(i),
data=firing_num_mat.squeeze().cpu().numpy(),
)
if flag:
if args.arch == "A":
layer = 3
flag = False
_, predicted = torch.max(output.data, 1)
_, labelTest = torch.max(labels.data, 1)
for i in range(b):
predicted_clips = predicted[i * args.clip : (i + 1) * args.clip]
labelTest_clips = labelTest[i * args.clip : (i + 1) * args.clip]
test_clip_correct = (predicted_clips == labelTest_clips).sum().item()
if test_clip_correct / args.clip > 0.5:
args.test_correct += 1
args.test_acc = 100.0 * float(args.test_correct) / (float(n_iter + 1) * b)
print(
"iteration: {}/{}\tacc: {}".format(
n_iter + 1, len(args.test_loader), args.test_acc
)
)
all_idx += 1
g = h5py.File(os.path.join(args.path, "all.h5"), "r")
f = h5py.File(os.path.join(args.path, "mean.h5"), "w")
names = []
data_all = []
for pre in ["S"]:
for nex in range(layer):
data_all.append([])
names.append(pre + "_" + str(nex))
for name in names:
start_time = time.time()
X = []
for idx in range(len(args.test_loader)):
if idx == 0:
X = g[str(idx) + "_" + name][()]
else:
X = X + g[str(idx) + "_" + name][()]
# X.append(g[str(idx) + '_' + name][()])
# X = np.array(X)
f.create_dataset(name, data=X / len(args.test_dataset))
# f1.create_dataset(name, data=np.std(X, axis=0))
print("costs:", time.time() - start_time)
f.close()
@torch.no_grad()
def mp_num(args):
args.model.load_state_dict(torch.load(args.modelPath))
args.model.eval()
save_name_file = os.path.join(args.name, "S_mp")
args.path = save_name_file
if not os.path.exists(save_name_file):
os.makedirs(save_name_file)
f = h5py.File(os.path.join(save_name_file, "all.h5"), "w")
all_idx = 0
flag = True
for n_iter, (input, labels) in enumerate(args.test_loader):
functional.reset_net(args.model)
b = input.size()[0]
input = input.reshape(
b * args.clip,
input.size()[2],
input.size()[3],
input.size()[4],
input.size()[5],
)
input = input.float().to(args.device)
if len(labels.shape) == 3:
labels = labels.reshape(b * args.clip, labels.size()[2])
labels = labels.float().to(args.device)
else:
labels = labels.reshape(b * args.clip, labels.size()[2], labels.size()[3])
labels = labels[:, 1, :].float().to(args.device)
output, _, mps = args.model(input=input, mp_collect=True)
if flag:
if args.arch == "A":
layer = 3
elif args.arch == "B":
layer = 5
flag = False
for i in range(len(mps)):
if i < layer:
mp = mps[i].reshape(
b * args.clip,
-1,
mps[i].size()[2],
mps[i].size()[3],
mps[i].size()[4],
)
# print(np.sum(mp.cpu().numpy()>0.3) / mp.numel())
f.create_dataset(
str(all_idx) + "_S_" + str(i), data=mp.squeeze().cpu().numpy()
)
_, predicted = torch.max(output.data, 1)
_, labelTest = torch.max(labels.data, 1)
for i in range(b):
predicted_clips = predicted[i * args.clip : (i + 1) * args.clip]
labelTest_clips = labelTest[i * args.clip : (i + 1) * args.clip]
test_clip_correct = (predicted_clips == labelTest_clips).sum().item()
if test_clip_correct / args.clip > 0.5:
args.test_correct += 1
args.test_acc = 100.0 * float(args.test_correct) / float(n_iter + 1)
print(
"iteration: {}/{}\tacc: {}".format(
n_iter + 1, len(args.test_dataset), args.test_acc
)
)
all_idx += 1
g = h5py.File(os.path.join(args.path, "all.h5"), "r")
f = h5py.File(os.path.join(args.path, "mean.h5"), "w")
names = []
data_all = []
for pre in ["S"]:
for nex in range(layer):
data_all.append([])
names.append(pre + "_" + str(nex))
for name in names:
start_time = time.time()
X = []
for idx in range(len(args.test_dataset)):
if idx == 0:
X = g[str(idx) + "_" + name][()]
else:
X = X + g[str(idx) + "_" + name][()]
# X.append(g[str(idx) + '_' + name][()])
# X = np.array(X)
# print(np.sum(X / len(args.test_dataset)>=0.3) / np.size(X))
f.create_dataset(name, data=X / len(args.test_dataset))
# f1.create_dataset(name, data=np.std(X, axis=0))
print("costs:", time.time() - start_time)
f.close()
def mean_std_firing(args):
# args.path = os.path.join(args.name, "S_Feature")
# print(os.path.join(args.path, args.mean_std + '.h5'))
f = h5py.File(os.path.join(args.path, args.mean_std + ".h5"), "r")
save_path_mean = args.path + os.sep + args.mean_std
save_path_mean_name = os.path.join(save_path_mean, args.name)
if not os.path.exists(save_path_mean_name):
os.makedirs(save_path_mean_name)
data_ = f[args.feature_name][()]
f.close()
_, T, C, _, _ = data_.shape
for t in range(T):
for c in range(C):
data = np.flipud(data_[0, t, c, :, :][()].T)
# print(data.shape)
if data.shape[0] == 32:
fig = plt.figure(figsize=(50, 50), clear=True)
elif data.shape[0] == 16:
fig = plt.figure(figsize=(25, 25), clear=True)
elif data.shape[0] == 8:
fig = plt.figure(figsize=(12, 12), clear=True)
else:
fig = plt.figure(figsize=(5, 5), clear=True)
ax = sns.heatmap(
data=data,
annot=True,
fmt=".2f",
cbar=False,
cmap="RdBu_r",
vmin=data.min(),
vmax=data.max(),
)
plt.xlabel("max:" + str(data.max()) + "_min:" + str(data.min()))
plt.tight_layout()
plt.title("C_" + str(c) + "T_" + str(t))
# plt.show()
plt.savefig(
os.path.join(
save_path_mean_name, "C_" + str(c) + "T_" + str(t) + ".png"
),
bbox_inches="tight",
pad_inches=0,
)
plt.clf()
plt.cla()
plt.close(fig)
def mean_std_mp(args):
# args.path = os.path.join(args.name, "S_Feature")
# print(os.path.join(args.path, args.mean_std + '.h5'))
f = h5py.File(os.path.join(args.path, args.mean_std + ".h5"), "r")
save_path_mean = args.path + os.sep + args.mean_std
save_path_mean_name = os.path.join(save_path_mean, args.feature_name)
if not os.path.exists(save_path_mean_name):
os.makedirs(save_path_mean_name)
data_ = f[args.feature_name][()]
f.close()
_, T, C, _, _ = data_.shape
spike = []
for t in range(T):
for c in range(C):
data = np.flipud(data_[0, t, c, :, :][()].T)
spike.append(np.sum(data >= 0.3) / np.size(data))
fig = plt.figure(figsize=(5, 5), clear=True)
sns.distplot(data, bins=50, kde=False)
plt.xlabel("max:" + str(data.max()) + "_min:" + str(data.min()))
plt.tight_layout()
plt.title("C_" + str(c) + "T_" + str(t))
# plt.show()
plt.savefig(
os.path.join(
save_path_mean_name, "C_" + str(c) + "T_" + str(t) + ".png"
),
bbox_inches="tight",
pad_inches=0,
)
print("C_" + str(c) + "T_" + str(t))
plt.clf()
plt.cla()
plt.close(fig)
def main():
args = parser.parse_args()
set_seed(args.seed)
args.test_correct = 0.0
args.recordPath = args.modelPath
args.im_width, args.im_height = (128 // args.ds, 128 // args.ds)
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.device_ids = range(torch.cuda.device_count())
# args.name = args.dataset + '_dt=' + str(args.dt) + 'ms' + '_T=' + str(args.T) + '_attn=' + args.attention
args.name = (
args.dataset
+ "_dt="
+ str(args.dt)
+ "ms"
+ "_T="
+ str(args.T)
+ "_attn="
+ args.attention
+ "_seed="
+ str(args.seed)
+ "_arch="
+ args.arch
)
args.modelNames = args.name + ".pth"
args.recordNames = args.name + ".csv"
print(args)
create_dataset(args=args)
create_net(args=args)
if args.collect == "firing":
firing_num(args=args)
# args.mean_std = "mean"
# mean_std_firing(args=args)
elif args.collect == "mp":
mp_num(args=args)
# args.mean_std = "mean"
# mean_std_mp(args=args)
if __name__ == "__main__":
main()