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import os
import numpy as np
import random
import torch
import shutil
import csv
import pprint
import pandas as pd
from loguru import logger
from collections import OrderedDict
import matplotlib.pyplot as plt
import pickle
import time
import numpy as np
def adjust_array(x, k):
len_x = len(x)
len_k = len(k)
# If x is shorter than k, pad with zeros
if len_x < len_k:
return np.pad(x, (0, len_k - len_x), 'constant')
# If x is longer than k, truncate x
elif len_x > len_k:
return x[:len_k]
# If both are of same length
else:
return x
def onset_to_frame(onset_times, audio_length, fps):
# Calculate total number of frames for the given audio length
total_frames = int(audio_length * fps)
# Create an array of zeros of shape (total_frames,)
frame_array = np.zeros(total_frames, dtype=np.int32)
# For each onset time, calculate the frame number and set it to 1
for onset in onset_times:
frame_num = int(onset * fps)
# Check if the frame number is within the array bounds
if 0 <= frame_num < total_frames:
frame_array[frame_num] = 1
return frame_array
def smooth_animations(animation1, animation2, blend_frames):
"""
Smoothly transition between two animation clips using linear interpolation.
Parameters:
- animation1: The first animation clip, a numpy array of shape [n, k].
- animation2: The second animation clip, a numpy array of shape [n, k].
- blend_frames: Number of frames over which to blend the two animations.
Returns:
- A smoothly blended animation clip of shape [2n, k].
"""
# Ensure blend_frames doesn't exceed the length of either animation
blend_frames = min(blend_frames, len(animation1), len(animation2))
# Extract overlapping sections
overlap_a1 = animation1[-blend_frames:-blend_frames+1, :]
overlap_a2 = animation2[blend_frames-1:blend_frames, :]
# Create blend weights for linear interpolation
alpha = np.linspace(0, 1, 2 * blend_frames).reshape(-1, 1)
# Linearly interpolate between overlapping sections
blended_overlap = overlap_a1 * (1 - alpha) + overlap_a2 * alpha
# Extend the animations to form the result with 2n frames
if blend_frames == len(animation1) and blend_frames == len(animation2):
result = blended_overlap
else:
before_blend = animation1[:-blend_frames]
after_blend = animation2[blend_frames:]
result = np.vstack((before_blend, blended_overlap, after_blend))
return result
def interpolate_sequence(quaternions):
bs, n, j, _ = quaternions.shape
new_n = 2 * n
new_quaternions = torch.zeros((bs, new_n, j, 4), device=quaternions.device, dtype=quaternions.dtype)
for i in range(n):
q1 = quaternions[:, i, :, :]
new_quaternions[:, 2*i, :, :] = q1
if i < n - 1:
q2 = quaternions[:, i + 1, :, :]
new_quaternions[:, 2*i + 1, :, :] = slerp(q1, q2, 0.5)
else:
# For the last point, duplicate the value
new_quaternions[:, 2*i + 1, :, :] = q1
return new_quaternions
def quaternion_multiply(q1, q2):
w1, x1, y1, z1 = q1
w2, x2, y2, z2 = q2
w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2
z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2
return w, x, y, z
def quaternion_conjugate(q):
w, x, y, z = q
return (w, -x, -y, -z)
def slerp(q1, q2, t):
dot = torch.sum(q1 * q2, dim=-1, keepdim=True)
flip = (dot < 0).float()
q2 = (1 - flip * 2) * q2
dot = dot * (1 - flip * 2)
DOT_THRESHOLD = 0.9995
mask = (dot > DOT_THRESHOLD).float()
theta_0 = torch.acos(dot)
theta = theta_0 * t
q3 = q2 - q1 * dot
q3 = q3 / torch.norm(q3, dim=-1, keepdim=True)
interpolated = (torch.cos(theta) * q1 + torch.sin(theta) * q3)
return mask * (q1 + t * (q2 - q1)) + (1 - mask) * interpolated
def estimate_linear_velocity(data_seq, dt):
'''
Given some batched data sequences of T timesteps in the shape (B, T, ...), estimates
the velocity for the middle T-2 steps using a second order central difference scheme.
The first and last frames are with forward and backward first-order
differences, respectively
- h : step size
'''
# first steps is forward diff (t+1 - t) / dt
init_vel = (data_seq[:, 1:2] - data_seq[:, :1]) / dt
# middle steps are second order (t+1 - t-1) / 2dt
middle_vel = (data_seq[:, 2:] - data_seq[:, 0:-2]) / (2 * dt)
# last step is backward diff (t - t-1) / dt
final_vel = (data_seq[:, -1:] - data_seq[:, -2:-1]) / dt
vel_seq = torch.cat([init_vel, middle_vel, final_vel], dim=1)
return vel_seq
def estimate_angular_velocity(rot_seq, dt):
'''
Given a batch of sequences of T rotation matrices, estimates angular velocity at T-2 steps.
Input sequence should be of shape (B, T, ..., 3, 3)
'''
# see https://en.wikipedia.org/wiki/Angular_velocity#Calculation_from_the_orientation_matrix
dRdt = estimate_linear_velocity(rot_seq, dt)
R = rot_seq
RT = R.transpose(-1, -2)
# compute skew-symmetric angular velocity tensor
w_mat = torch.matmul(dRdt, RT)
# pull out angular velocity vector by averaging symmetric entries
w_x = (-w_mat[..., 1, 2] + w_mat[..., 2, 1]) / 2.0
w_y = (w_mat[..., 0, 2] - w_mat[..., 2, 0]) / 2.0
w_z = (-w_mat[..., 0, 1] + w_mat[..., 1, 0]) / 2.0
w = torch.stack([w_x, w_y, w_z], axis=-1)
return w
import matplotlib.image as mpimg
from io import BytesIO
def image_from_bytes(image_bytes):
return mpimg.imread(BytesIO(image_bytes), format='PNG')
def process_frame(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import trimesh
import pyvirtualdisplay as Display
vertices = vertices_all[i]
vertices1 = vertices1_all[i]
filename = f"{output_dir}frame_{i}.png"
filenames.append(filename)
if i%100 == 0:
print('processed', i, 'frames')
#time_s = time.time()
#print(vertices.shape)
if use_matplotlib:
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(121, projection="3d")
fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
#ax.view_init(elev=0, azim=90)
x = vertices[:, 0]
y = vertices[:, 1]
z = vertices[:, 2]
ax.scatter(x, y, z, s=0.5)
ax.set_xlim([-1.0, 1.0])
ax.set_ylim([-0.5, 1.5])#heigth
ax.set_zlim([-0, 2])#depth
ax.set_box_aspect((1,1,1))
else:
mesh = trimesh.Trimesh(vertices, faces)
scene = mesh.scene()
scene.camera.fov = camera_params['fov']
scene.camera.resolution = camera_params['resolution']
scene.camera.z_near = camera_params['z_near']
scene.camera.z_far = camera_params['z_far']
scene.graph[scene.camera.name] = camera_params['transform']
fig, ax =plt.subplots(1,2, figsize=(16, 6))
image = scene.save_image(resolution=[640, 480], visible=False)
im0 = ax[0].imshow(image_from_bytes(image))
ax[0].axis('off')
if use_matplotlib:
ax2 = fig.add_subplot(122, projection="3d")
ax2.set_box_aspect((1,1,1))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
x1 = vertices1[:, 0]
y1 = vertices1[:, 1]
z1 = vertices1[:, 2]
ax2.scatter(x1, y1, z1, s=0.5)
ax2.set_xlim([-1.0, 1.0])
ax2.set_ylim([-0.5, 1.5])#heigth
ax2.set_zlim([-0, 2])
plt.savefig(filename, bbox_inches='tight')
plt.close(fig)
else:
mesh1 = trimesh.Trimesh(vertices1, faces)
scene1 = mesh1.scene()
scene1.camera.fov = camera_params1['fov']
scene1.camera.resolution = camera_params1['resolution']
scene1.camera.z_near = camera_params1['z_near']
scene1.camera.z_far = camera_params1['z_far']
scene1.graph[scene1.camera.name] = camera_params1['transform']
image1 = scene1.save_image(resolution=[640, 480], visible=False)
im1 = ax[1].imshow(image_from_bytes(image1))
ax[1].axis('off')
plt.savefig(filename, bbox_inches='tight')
plt.close(fig)
def generate_images(frames, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames):
import multiprocessing
import trimesh
num_cores = multiprocessing.cpu_count() # This will get the number of cores on your machine.
mesh = trimesh.Trimesh(vertices_all[0], faces)
scene = mesh.scene()
camera_params = {
'fov': scene.camera.fov,
'resolution': scene.camera.resolution,
'focal': scene.camera.focal,
'z_near': scene.camera.z_near,
"z_far": scene.camera.z_far,
'transform': scene.graph[scene.camera.name][0]
}
mesh1 = trimesh.Trimesh(vertices1_all[0], faces)
scene1 = mesh1.scene()
camera_params1 = {
'fov': scene1.camera.fov,
'resolution': scene1.camera.resolution,
'focal': scene1.camera.focal,
'z_near': scene1.camera.z_near,
"z_far": scene1.camera.z_far,
'transform': scene1.graph[scene1.camera.name][0]
}
# Use a Pool to manage the processes
# print(num_cores)
progress = multiprocessing.Value('i', 0)
lock = multiprocessing.Lock()
with multiprocessing.Pool(num_cores) as pool:
pool.starmap(process_frame, [(i, vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames, camera_params, camera_params1) for i in range(frames)])
def render_one_sequence(
res_npz_path,
gt_npz_path,
output_dir,
audio_path,
model_folder="/data/datasets/smplx_models/",
model_type='smplx',
gender='NEUTRAL_2020',
ext='npz',
num_betas=300,
num_expression_coeffs=100,
use_face_contour=False,
use_matplotlib=False,
args=None):
import smplx
import matplotlib.pyplot as plt
import imageio
from tqdm import tqdm
import os
import numpy as np
import torch
import moviepy.editor as mp
import librosa
model = smplx.create(model_folder, model_type=model_type,
gender=gender, use_face_contour=use_face_contour,
num_betas=num_betas,
num_expression_coeffs=num_expression_coeffs,
ext=ext, use_pca=False).cuda()
#data_npz = np.load(f"{output_dir}{res_npz_path}.npz")
data_np_body = np.load(res_npz_path, allow_pickle=True)
gt_np_body = np.load(gt_npz_path, allow_pickle=True)
if not os.path.exists(output_dir): os.makedirs(output_dir)
filenames = []
if not use_matplotlib:
import trimesh
#import pyrender
from pyvirtualdisplay import Display
display = Display(visible=0, size=(640, 480))
display.start()
faces = np.load(f"{model_folder}/smplx/SMPLX_NEUTRAL_2020.npz", allow_pickle=True)["f"]
seconds = 1
#data_npz["jaw_pose"].shape[0]
n = data_np_body["poses"].shape[0]
beta = torch.from_numpy(data_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda()
beta = beta.repeat(n, 1)
expression = torch.from_numpy(data_np_body["expressions"][:n]).to(torch.float32).cuda()
jaw_pose = torch.from_numpy(data_np_body["poses"][:n, 66:69]).to(torch.float32).cuda()
pose = torch.from_numpy(data_np_body["poses"][:n]).to(torch.float32).cuda()
transl = torch.from_numpy(data_np_body["trans"][:n]).to(torch.float32).cuda()
# print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape, pose[:,:3].shape)
output = model(betas=beta, transl=transl, expression=expression, jaw_pose=jaw_pose,
global_orient=pose[:,:3], body_pose=pose[:,3:21*3+3], left_hand_pose=pose[:,25*3:40*3], right_hand_pose=pose[:,40*3:55*3],
leye_pose=pose[:, 69:72],
reye_pose=pose[:, 72:75],
return_verts=True)
vertices_all = output["vertices"].cpu().detach().numpy()
beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0).cuda()
expression1 = torch.from_numpy(gt_np_body["expressions"][:n]).to(torch.float32).cuda()
jaw_pose1 = torch.from_numpy(gt_np_body["poses"][:n,66:69]).to(torch.float32).cuda()
pose1 = torch.from_numpy(gt_np_body["poses"][:n]).to(torch.float32).cuda()
transl1 = torch.from_numpy(gt_np_body["trans"][:n]).to(torch.float32).cuda()
output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[:,:3], body_pose=pose1[:,3:21*3+3], left_hand_pose=pose1[:,25*3:40*3], right_hand_pose=pose1[:,40*3:55*3],
leye_pose=pose1[:, 69:72],
reye_pose=pose1[:, 72:75],return_verts=True)
vertices1_all = output1["vertices"].cpu().detach().numpy()
if args.debug:
seconds = 1
else:
seconds = vertices_all.shape[0]//30
# camera_settings = None
time_s = time.time()
generate_images(int(seconds*30), vertices_all, vertices1_all, faces, output_dir, use_matplotlib, filenames)
filenames = [f"{output_dir}frame_{i}.png" for i in range(int(seconds*30))]
# print(time.time()-time_s)
# for i in tqdm(range(seconds*30)):
# vertices = vertices_all[i]
# vertices1 = vertices1_all[i]
# filename = f"{output_dir}frame_{i}.png"
# filenames.append(filename)
# #time_s = time.time()
# #print(vertices.shape)
# if use_matplotlib:
# fig = plt.figure(figsize=(20, 10))
# ax = fig.add_subplot(121, projection="3d")
# fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
# #ax.view_init(elev=0, azim=90)
# x = vertices[:, 0]
# y = vertices[:, 1]
# z = vertices[:, 2]
# ax.scatter(x, y, z, s=0.5)
# ax.set_xlim([-1.0, 1.0])
# ax.set_ylim([-0.5, 1.5])#heigth
# ax.set_zlim([-0, 2])#depth
# ax.set_box_aspect((1,1,1))
# else:
# mesh = trimesh.Trimesh(vertices, faces)
# if i == 0:
# scene = mesh.scene()
# camera_params = {
# 'fov': scene.camera.fov,
# 'resolution': scene.camera.resolution,
# 'focal': scene.camera.focal,
# 'z_near': scene.camera.z_near,
# "z_far": scene.camera.z_far,
# 'transform': scene.graph[scene.camera.name][0]
# }
# else:
# scene = mesh.scene()
# scene.camera.fov = camera_params['fov']
# scene.camera.resolution = camera_params['resolution']
# scene.camera.z_near = camera_params['z_near']
# scene.camera.z_far = camera_params['z_far']
# scene.graph[scene.camera.name] = camera_params['transform']
# fig, ax =plt.subplots(1,2, figsize=(16, 6))
# image = scene.save_image(resolution=[640, 480], visible=False)
# #print((time.time()-time_s))
# im0 = ax[0].imshow(image_from_bytes(image))
# ax[0].axis('off')
# # beta1 = torch.from_numpy(gt_np_body["betas"]).to(torch.float32).unsqueeze(0)
# # expression1 = torch.from_numpy(gt_np_body["expressions"][i]).to(torch.float32).unsqueeze(0)
# # jaw_pose1 = torch.from_numpy(gt_np_body["poses"][i][66:69]).to(torch.float32).unsqueeze(0)
# # pose1 = torch.from_numpy(gt_np_body["poses"][i]).to(torch.float32).unsqueeze(0)
# # transl1 = torch.from_numpy(gt_np_body["trans"][i]).to(torch.float32).unsqueeze(0)
# # #print(beta.shape, expression.shape, jaw_pose.shape, pose.shape, transl.shape)global_orient=pose[0:1,:3],
# # output1 = model(betas=beta1, transl=transl1, expression=expression1, jaw_pose=jaw_pose1, global_orient=pose1[0:1,:3], body_pose=pose1[0:1,3:21*3+3], left_hand_pose=pose1[0:1,25*3:40*3], right_hand_pose=pose1[0:1,40*3:55*3], return_verts=True)
# # vertices1 = output1["vertices"].cpu().detach().numpy()[0]
# if use_matplotlib:
# ax2 = fig.add_subplot(122, projection="3d")
# ax2.set_box_aspect((1,1,1))
# fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
# #ax2.view_init(elev=0, azim=90)
# x1 = vertices1[:, 0]
# y1 = vertices1[:, 1]
# z1 = vertices1[:, 2]
# ax2.scatter(x1, y1, z1, s=0.5)
# ax2.set_xlim([-1.0, 1.0])
# ax2.set_ylim([-0.5, 1.5])#heigth
# ax2.set_zlim([-0, 2])
# plt.savefig(filename, bbox_inches='tight')
# plt.close(fig)
# else:
# mesh1 = trimesh.Trimesh(vertices1, faces)
# if i == 0:
# scene1 = mesh1.scene()
# camera_params1 = {
# 'fov': scene1.camera.fov,
# 'resolution': scene1.camera.resolution,
# 'focal': scene1.camera.focal,
# 'z_near': scene1.camera.z_near,
# "z_far": scene1.camera.z_far,
# 'transform': scene1.graph[scene1.camera.name][0]
# }
# else:
# scene1 = mesh1.scene()
# scene1.camera.fov = camera_params1['fov']
# scene1.camera.resolution = camera_params1['resolution']
# scene1.camera.z_near = camera_params1['z_near']
# scene1.camera.z_far = camera_params1['z_far']
# scene1.graph[scene1.camera.name] = camera_params1['transform']
# image1 = scene1.save_image(resolution=[640, 480], visible=False)
# im1 = ax[1].imshow(image_from_bytes(image1))
# ax[1].axis('off')
# plt.savefig(filename, bbox_inches='tight')
# plt.close(fig)
# display.stop()
# print(filenames)
images = [imageio.imread(filename) for filename in filenames]
imageio.mimsave(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4", images, fps=30)
for filename in filenames:
os.remove(filename)
video = mp.VideoFileClip(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4")
# audio, sr = librosa.load(audio_path)
# audio = audio[:seconds*sr]
# print(audio.shape, seconds, sr)
# import soundfile as sf
# sf.write(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, 16000, 'PCM_24')
# audio_tmp = librosa.output.write_wav(f"{output_dir}{res_npz_path.split('/')[-1][:-4]}.wav", audio, sr=16000)
audio = mp.AudioFileClip(audio_path)
if audio.duration > video.duration:
audio = audio.subclip(0, video.duration)
final_clip = video.set_audio(audio)
final_clip.write_videofile(f"{output_dir}{res_npz_path.split('/')[-1][4:-4]}.mp4")
os.remove(f"{output_dir}raw_{res_npz_path.split('/')[-1][:-4]}.mp4")
def print_exp_info(args):
logger.info(pprint.pformat(vars(args)))
logger.info(f"# ------------ {args.name} ----------- #")
logger.info("PyTorch version: {}".format(torch.__version__))
logger.info("CUDA version: {}".format(torch.version.cuda))
logger.info("{} GPUs".format(torch.cuda.device_count()))
logger.info(f"Random Seed: {args.random_seed}")
def args2csv(args, get_head=False, list4print=[]):
for k, v in args.items():
if isinstance(args[k], dict):
args2csv(args[k], get_head, list4print)
else: list4print.append(k) if get_head else list4print.append(v)
return list4print
class EpochTracker:
def __init__(self, metric_names, metric_directions):
assert len(metric_names) == len(metric_directions), "Metric names and directions should have the same length"
self.metric_names = metric_names
self.states = ['train', 'val', 'test']
self.types = ['last', 'best']
self.values = {name: {state: {type_: {'value': np.inf if not is_higher_better else -np.inf, 'epoch': 0}
for type_ in self.types}
for state in self.states}
for name, is_higher_better in zip(metric_names, metric_directions)}
self.loss_meters = {name: {state: AverageMeter(f"{name}_{state}")
for state in self.states}
for name in metric_names}
self.is_higher_better = {name: direction for name, direction in zip(metric_names, metric_directions)}
self.train_history = {name: [] for name in metric_names}
self.val_history = {name: [] for name in metric_names}
def update_meter(self, name, state, value):
self.loss_meters[name][state].update(value)
def update_values(self, name, state, epoch):
value_avg = self.loss_meters[name][state].avg
new_best = False
if ((value_avg < self.values[name][state]['best']['value'] and not self.is_higher_better[name]) or
(value_avg > self.values[name][state]['best']['value'] and self.is_higher_better[name])):
self.values[name][state]['best']['value'] = value_avg
self.values[name][state]['best']['epoch'] = epoch
new_best = True
self.values[name][state]['last']['value'] = value_avg
self.values[name][state]['last']['epoch'] = epoch
return new_best
def get(self, name, state, type_):
return self.values[name][state][type_]
def reset(self):
for name in self.metric_names:
for state in self.states:
self.loss_meters[name][state].reset()
def flatten_values(self):
flat_dict = {}
for name in self.metric_names:
for state in self.states:
for type_ in self.types:
value_key = f"{name}_{state}_{type_}"
epoch_key = f"{name}_{state}_{type_}_epoch"
flat_dict[value_key] = self.values[name][state][type_]['value']
flat_dict[epoch_key] = self.values[name][state][type_]['epoch']
return flat_dict
def update_and_plot(self, name, epoch, save_path):
new_best_train = self.update_values(name, 'train', epoch)
new_best_val = self.update_values(name, 'val', epoch)
self.train_history[name].append(self.loss_meters[name]['train'].avg)
self.val_history[name].append(self.loss_meters[name]['val'].avg)
train_values = self.train_history[name]
val_values = self.val_history[name]
epochs = list(range(1, len(train_values) + 1))
plt.figure(figsize=(10, 6))
plt.plot(epochs, train_values, label='Train')
plt.plot(epochs, val_values, label='Val')
plt.title(f'Train vs Val {name} over epochs')
plt.xlabel('Epochs')
plt.ylabel(name)
plt.legend()
plt.savefig(save_path)
plt.close()
return new_best_train, new_best_val
def record_trial(args, tracker):
"""
1. record notes, score, env_name, experments_path,
"""
csv_path = args.out_path + "custom/" +args.csv_name+".csv"
all_print_dict = vars(args)
all_print_dict.update(tracker.flatten_values())
if not os.path.exists(csv_path):
pd.DataFrame([all_print_dict]).to_csv(csv_path, index=False)
else:
df_existing = pd.read_csv(csv_path)
df_new = pd.DataFrame([all_print_dict])
df_aligned = df_existing.append(df_new).fillna("")
df_aligned.to_csv(csv_path, index=False)
def set_random_seed(args):
os.environ['PYTHONHASHSEED'] = str(args.random_seed)
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed_all(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
torch.backends.cudnn.deterministic = args.deterministic #args.CUDNN_DETERMINISTIC
torch.backends.cudnn.benchmark = args.benchmark
torch.backends.cudnn.enabled = args.cudnn_enabled
def save_checkpoints(save_path, model, opt=None, epoch=None, lrs=None):
if lrs is not None:
states = { 'model_state': model.state_dict(),
'epoch': epoch + 1,
'opt_state': opt.state_dict(),
'lrs':lrs.state_dict(),}
elif opt is not None:
states = { 'model_state': model.state_dict(),
'epoch': epoch + 1,
'opt_state': opt.state_dict(),}
else:
states = { 'model_state': model.state_dict(),}
torch.save(states, save_path)
def load_checkpoints(model, save_path, load_name='model'):
states = torch.load(save_path)
new_weights = OrderedDict()
flag=False
for k, v in states['model_state'].items():
#print(k)
if "module" not in k:
break
else:
new_weights[k[7:]]=v
flag=True
if flag:
try:
model.load_state_dict(new_weights)
except:
#print(states['model_state'])
model.load_state_dict(states['model_state'])
else:
model.load_state_dict(states['model_state'])
logger.info(f"load self-pretrained checkpoints for {load_name}")
def model_complexity(model, args):
from ptflops import get_model_complexity_info
flops, params = get_model_complexity_info(model, (args.T_GLOBAL._DIM, args.TRAIN.CROP, args.TRAIN),
as_strings=False, print_per_layer_stat=False)
logging.info('{:<30} {:<8} BFlops'.format('Computational complexity: ', flops / 1e9))
logging.info('{:<30} {:<8} MParams'.format('Number of parameters: ', params / 1e6))
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__) |