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c1596ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | 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]) |