WeSign/sign-predictor
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Fixed some bugs in the training loop, still no good results

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Victor Mylle
2023-02-23 12:06:26 +00:00
parent 98f29f683e
commit 97ede38e3a
5 changed files with 68 additions and 63 deletions
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1
requirements.txt
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@@ -3,3 +3,4 @@ torchvision
pandas pandas
mediapipe mediapipe
joblib joblib
tensorboard

9
src/dataset.py
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@@ -1,9 +1,12 @@
import torch
import json import json
from keypoint_extractor import KeypointExtractor
from collections import OrderedDict from collections import OrderedDict
from identifiers import LANDMARKS
import numpy as np import numpy as np
import torch
from identifiers import LANDMARKS
from keypoint_extractor import KeypointExtractor
class WLASLDataset(torch.utils.data.Dataset): class WLASLDataset(torch.utils.data.Dataset):
def __init__(self, json_file: str, missing: str, keypoint_extractor: KeypointExtractor, subset:str="train", keypoints_identifier: dict = None, transform=None): def __init__(self, json_file: str, missing: str, keypoint_extractor: KeypointExtractor, subset:str="train", keypoints_identifier: dict = None, transform=None):

42
src/identifiers.py
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@@ -1,38 +1,38 @@
LANDMARKS = { LANDMARKS = {
# Pose Landmarks # Pose Landmarks
"nose": 0, "nose": 0,
"left_eye_inner": 1, # "left_eye_inner": 1,
"left_eye": 2, "left_eye": 2,
"left_eye_outer": 3, # "left_eye_outer": 3,
"right_eye_inner": 4, # "right_eye_inner": 4,
"right_eye": 5, "right_eye": 5,
"right_eye_outer": 6, # "right_eye_outer": 6,
"left_ear": 7, "left_ear": 7,
"right_ear": 8, "right_ear": 8,
"mouth_left": 9, "mouth_left": 9,
"mouth_right": 10, # "mouth_right": 10,
"left_shoulder": 11, "left_shoulder": 11,
"right_shoulder": 12, "right_shoulder": 12,
"left_elbow": 13, "left_elbow": 13,
"right_elbow": 14, "right_elbow": 14,
"left_wrist": 15, "left_wrist": 15,
"right_wrist": 16, "right_wrist": 16,
"left_pinky": 17, # "left_pinky": 17,
"right_pinky": 18, # "right_pinky": 18,
"left_index": 19, # "left_index": 19,
"right_index": 20, # "right_index": 20,
"left_thumb": 21, # "left_thumb": 21,
"right_thumb": 22, # "right_thumb": 22,
"left_hip": 23, # "left_hip": 23,
"right_hip": 24, # "right_hip": 24,
"left_knee": 25, # "left_knee": 25,
"right_knee": 26, # "right_knee": 26,
"left_ankle": 27, # "left_ankle": 27,
"right_ankle": 28, # "right_ankle": 28,
"left_heel": 29, # "left_heel": 29,
"right_heel": 30, # "right_heel": 30,
"left_foot_index": 31, # "left_foot_index": 31,
"right_foot_index": 32, # "right_foot_index": 32,
# Left Hand Landmarks # Left Hand Landmarks
"left_wrist2": 33, "left_wrist2": 33,

9
src/model.py
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@@ -1,11 +1,11 @@
### SPOTER model implementation from the paper "SPOTER: Sign Pose-based Transformer for Sign Language Recognition from Sequence of Skeletal Data" ### SPOTER model implementation from the paper "SPOTER: Sign Pose-based Transformer for Sign Language Recognition from Sequence of Skeletal Data"
import copy import copy
import torch
import torch.nn as nn
from typing import Optional from typing import Optional
import torch
import torch.nn as nn
def _get_clones(mod, n): def _get_clones(mod, n):
return nn.ModuleList([copy.deepcopy(mod) for _ in range(n)]) return nn.ModuleList([copy.deepcopy(mod) for _ in range(n)])
@@ -51,7 +51,7 @@ class SPOTER(nn.Module):
self.row_embed = nn.Parameter(torch.rand(50, hidden_dim)) self.row_embed = nn.Parameter(torch.rand(50, hidden_dim))
self.pos = nn.Parameter(torch.cat([self.row_embed[0].unsqueeze(0).repeat(1, 1, 1)], dim=-1).flatten(0, 1).unsqueeze(0)) self.pos = nn.Parameter(torch.cat([self.row_embed[0].unsqueeze(0).repeat(1, 1, 1)], dim=-1).flatten(0, 1).unsqueeze(0))
self.class_query = nn.Parameter(torch.rand(1, hidden_dim)) self.class_query = nn.Parameter(torch.rand(1, hidden_dim))
self.transformer = nn.Transformer(hidden_dim, 10, 6, 6) self.transformer = nn.Transformer(hidden_dim, 9, 6, 6)
self.linear_class = nn.Linear(hidden_dim, num_classes) self.linear_class = nn.Linear(hidden_dim, num_classes)
# Deactivate the initial attention decoder mechanism # Deactivate the initial attention decoder mechanism
@@ -61,7 +61,6 @@ class SPOTER(nn.Module):
def forward(self, inputs): def forward(self, inputs):
h = torch.unsqueeze(inputs.flatten(start_dim=1), 1).float() h = torch.unsqueeze(inputs.flatten(start_dim=1), 1).float()
h = self.transformer(self.pos + h, self.class_query.unsqueeze(0)).transpose(0, 1) h = self.transformer(self.pos + h, self.class_query.unsqueeze(0)).transpose(0, 1)
res = self.linear_class(h) res = self.linear_class(h)

66
src/train.py
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@@ -1,22 +1,23 @@
import os
import argparse import argparse
import random
import logging import logging
import torch import os
import random
import numpy as np
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from torchvision import transforms
from torch.utils.data import DataLoader
from pathlib import Path from pathlib import Path
from keypoint_extractor import KeypointExtractor import matplotlib.pyplot as plt
from identifiers import LANDMARKS import matplotlib.ticker as ticker
from model import SPOTER import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms
from dataset import WLASLDataset from dataset import WLASLDataset
from identifiers import LANDMARKS
from keypoint_extractor import KeypointExtractor
from model import SPOTER
def train(): def train():
random.seed(379) random.seed(379)
@@ -31,7 +32,7 @@ def train():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
spoter_model = SPOTER(num_classes=100, hidden_dim=2*75) spoter_model = SPOTER(num_classes=100, hidden_dim=len(LANDMARKS) *2)
spoter_model.train(True) spoter_model.train(True)
spoter_model.to(device) spoter_model.to(device)
@@ -56,53 +57,54 @@ def train():
train_acc, val_acc = 0, 0 train_acc, val_acc = 0, 0
losses, train_accs, val_accs = [], [], []
lr_progress = [] lr_progress = []
top_train_acc, top_val_acc = 0, 0 top_train_acc, top_val_acc = 0, 0
checkpoint_index = 0 checkpoint_index = 0
for epoch in range(100): for epoch in range(100):
running_loss = 0.0
pred_correct, pred_all = 0, 0
# train # train
for i, (inputs, labels) in enumerate(train_loader): for i, (inputs, labels) in enumerate(train_loader):
inputs = inputs.squeeze(0).to(device) inputs = inputs.squeeze(0).to(device)
labels = labels.to(device) labels = labels.to(device, dtype=torch.long)
optimizer.zero_grad() optimizer.zero_grad()
outputs = spoter_model(inputs).expand(1, -1, -1) outputs = spoter_model(inputs).expand(1, -1, -1)
loss = criterion(outputs[0], labels) loss = criterion(outputs[0], labels)
loss.backward() loss.backward()
optimizer.step() optimizer.step()
running_loss += loss
_, predicted = torch.max(outputs.data, 1) if int(torch.argmax(torch.nn.functional.softmax(outputs, dim=2))) == int(labels[0]):
train_acc = (predicted == labels).sum().item() / labels.size(0) pred_correct += 1
pred_all += 1
losses.append(loss.item())
train_accs.append(train_acc)
if i % 100 == 0: if i % 100 == 0:
print(f"Epoch: {epoch} | Batch: {i} | Loss: {loss.item()} | Train Acc: {train_acc}") print(f"Epoch: {epoch} | Batch: {i} | Loss: {running_loss.item()} | Train Acc: {(pred_correct / pred_all)}")
if scheduler:
scheduler.step(running_loss.item() / len(train_loader))
# validate # validate
with torch.no_grad(): with torch.no_grad():
for i, (inputs, labels) in enumerate(val_loader): for i, (inputs, labels) in enumerate(val_loader):
inputs = inputs.to(device) inputs = inputs.squeeze(0).to(device)
labels = labels.to(device) labels = labels.to(device)
outputs = spoter_model(inputs) outputs = spoter_model(inputs)
_, predicted = torch.max(outputs.data, 1) _, predicted = torch.max(outputs.data, 1)
val_acc = (predicted == labels).sum().item() / labels.size(0) val_acc = (predicted == labels).sum().item() / labels.size(0)
val_accs.append(val_acc)
scheduler.step(loss)
# save checkpoint # save checkpoint
if val_acc > top_val_acc: # if val_acc > top_val_acc:
top_val_acc = val_acc # top_val_acc = val_acc
top_train_acc = train_acc # top_train_acc = train_acc
checkpoint_index = epoch # checkpoint_index = epoch
torch.save(spoter_model.state_dict(), f"checkpoints/spoter_{epoch}.pth") # torch.save(spoter_model.state_dict(), f"checkpoints/spoter_{epoch}.pth")
print(f"Epoch: {epoch} | Train Acc: {train_acc} | Val Acc: {val_acc}") print(f"Epoch: {epoch} | Train Acc: {train_acc} | Val Acc: {val_acc}")
lr_progress.append(optimizer.param_groups[0]['lr']) lr_progress.append(optimizer.param_groups[0]['lr'])