src/debug/test_forward.py

import sys
sys.path.append("/workspace/src/models")
import torch


# model imports
from lstm import DecoderLSTM
from gru import DecoderGRU
from transformer import DecoderTransformer
# from transformer_scratch import DecoderTransformer

from resnet18 import EncoderResnet18
from efficientnet import EncoderEfficientNetB0
from convnext import EncoderConvNextTiny
from mobilenet import EncoderMobileNetV3Small
from vit import EncoderViTB16
from swin import EncoderSwinTiny
from deit import EncoderDeiTTiny



# device
device = torch.device(
    "cuda" if torch.cuda.is_available() else "cpu"
)

print(f"device: {device}")



# caption model dummy input
feature = torch.randn(1, 512).to(device)
# feature = torch.randn(1, 49, 512).to(device)

caption = torch.tensor(
    [[0, 1, 2, 3, 4]]
).to(device)



### LSTM Forward ###
lstm_model = DecoderLSTM().to(device)

lstm_out = lstm_model(
    feature,
    caption
)

print(f"LSTM: {lstm_out.shape}")



### GRU Forward ###
gru_model = DecoderGRU().to(device)

gru_out = gru_model(
    feature,
    caption
)

print(f"GRU: {gru_out.shape}")



### Transformer Forward ###
transformer_model = DecoderTransformer().to(device)

transformer_out, map, map = transformer_model(
    caption,
    feature,
    0
)

print(f"Transformer: {transformer_out.shape}")



### ResNet18 Forward ###
NUM_CLASSES = 50

resnet18_model = EncoderResnet18(
    num_classes=NUM_CLASSES
).to(device)

dummy_images = torch.randn(
    8, 3, 224, 224
).to(device)

logits, features = resnet18_model(
    dummy_images
)

print(f"ResNet18 logits: {logits.shape}")
print(f"ResNet18 features: {features.shape}")



### EfficientNet-B0 Forward ###
efficientnet_model = EncoderEfficientNetB0(
    num_classes=NUM_CLASSES
).to(device)

efficientnet_out = efficientnet_model(
    dummy_images
)

print(
    f"EfficientNet-B0: "
    f"{efficientnet_out.shape}"
)

# expected:
# torch.Size([8, 50])



### ConvNeXt-Tiny Forward ###
convnext_model = EncoderConvNextTiny(
    num_classes=NUM_CLASSES
).to(device)

convnext_out = convnext_model(
    dummy_images
)

print(
    f"ConvNeXt-Tiny: "
    f"{convnext_out.shape}"
)

# expected:
# torch.Size([8, 50])



### MobileNetV3 Small Forward ###
mobilenet_model = EncoderMobileNetV3Small(
    num_classes=NUM_CLASSES
).to(device)

mobilenet_out = mobilenet_model(
    dummy_images
)

print(
    f"MobileNetV3 Small: "
    f"{mobilenet_out.shape}"
)

# expected:
# torch.Size([8, 50])



### ViT-B/16 Forward ###
vit_model = EncoderViTB16(
    num_classes=NUM_CLASSES
).to(device)

vit_out = vit_model(
    dummy_images
)

print(
    f"ViT-B/16: "
    f"{vit_out.shape}"
)

# expected:
# torch.Size([8, 50])



### Swin-T Forward ###
swin_model = EncoderSwinTiny(
    num_classes=NUM_CLASSES
).to(device)

swin_out = swin_model(
    dummy_images
)

print(
    f"Swin-T: "
    f"{swin_out.shape}"
)

# expected:
# torch.Size([8, 50])



### DeiT-Tiny Forward ###
deit_model = EncoderDeiTTiny(
    num_classes=NUM_CLASSES
).to(device)

deit_out = deit_model(
    dummy_images
)

print(
    f"DeiT-Tiny: "
    f"{deit_out.shape}"
)

# expected:
# torch.Size([8, 50])