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import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import torch
def parse_filelist(filelist_path, split_char="|"):
with open(filelist_path, encoding='utf-8') as f:
filepaths_and_text = [line.strip().split(split_char) for line in f]
return filepaths_and_text
def load_model(model, saved_state_dict):
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
try:
new_state_dict[k] = saved_state_dict[k]
except:
print("%s is not in the checkpoint" % k)
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
return model
def latest_checkpoint_path(dir_path, regex="grad_svc_*.pt"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
return x
def load_checkpoint(logdir, model, num=None):
if num is None:
model_path = latest_checkpoint_path(logdir, regex="grad_svc_*.pt")
else:
model_path = os.path.join(logdir, f"grad_svc_{num}.pt")
print(f'Loading checkpoint {model_path}...')
model_dict = torch.load(model_path, map_location=lambda loc, storage: loc)
model.load_state_dict(model_dict, strict=False)
return model
def save_figure_to_numpy(fig):
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return data
def plot_tensor(tensor):
plt.style.use('default')
fig, ax = plt.subplots(figsize=(12, 3))
im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none')
plt.colorbar(im, ax=ax)
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
def save_plot(tensor, savepath):
plt.style.use('default')
fig, ax = plt.subplots(figsize=(12, 3))
im = ax.imshow(tensor, aspect="auto", origin="lower", interpolation='none')
plt.colorbar(im, ax=ax)
plt.tight_layout()
fig.canvas.draw()
plt.savefig(savepath)
plt.close()
return
def print_error(info):
print(f"\033[31m {info} \033[0m")
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