COLORS = [
    [0.000, 0.447, 0.741],
    [0.850, 0.325, 0.098],
    [0.929, 0.694, 0.125],
    [0.494, 0.184, 0.556],
    [0.466, 0.674, 0.188],
    [0.301, 0.745, 0.933],
    [0.351, 0.760, 0.903],
]


MODELS_DETAILS = {
    "DETR-RESNET-50": """DetrForObjectDetection(
  (model): DetrModel(
    (backbone): DetrConvModel(
      (conv_encoder): DetrConvEncoder(
        (model): FeatureListNet(
          (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
          (bn1): DetrFrozenBatchNorm2d()
          (act1): ReLU(inplace=True)
          (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
          (layer1): Sequential(
            (0): Bottleneck(
              (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
              (downsample): Sequential(
                (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
                (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
            )
            (1): Bottleneck(
              (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (2): Bottleneck(
              (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
          )
          (layer2): Sequential(
            (0): Bottleneck(
              (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
              (downsample): Sequential(
                (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
            )
            (1): Bottleneck(
              (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (2): Bottleneck(
              (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (3): Bottleneck(
              (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
          )
          (layer3): Sequential(
            (0): Bottleneck(
              (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
              (downsample): Sequential(
                (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
            )
            (1): Bottleneck(
              (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (2): Bottleneck(
              (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (3): Bottleneck(
              (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (4): Bottleneck(
              (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (5): Bottleneck(
              (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
          )
          (layer4): Sequential(
            (0): Bottleneck(
              (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
              (downsample): Sequential(
                (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
                (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
              )
            )
            (1): Bottleneck(
              (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
            (2): Bottleneck(
              (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn1): DetrFrozenBatchNorm2d()
              (act1): ReLU(inplace=True)
              (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
              (bn2): DetrFrozenBatchNorm2d()
              (drop_block): Identity()
              (act2): ReLU(inplace=True)
              (aa): Identity()
              (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
              (bn3): DetrFrozenBatchNorm2d()
              (act3): ReLU(inplace=True)
            )
          )
        )
      )
      (position_embedding): DetrSinePositionEmbedding()
    )
    (input_projection): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (query_position_embeddings): Embedding(100, 256)
    (encoder): DetrEncoder(
      (layers): ModuleList(
        (0-5): 6 x DetrEncoderLayer(
          (self_attn): DetrAttention(
            (k_proj): Linear(in_features=256, out_features=256, bias=True)
            (v_proj): Linear(in_features=256, out_features=256, bias=True)
            (q_proj): Linear(in_features=256, out_features=256, bias=True)
            (out_proj): Linear(in_features=256, out_features=256, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (activation_fn): ReLU()
          (fc1): Linear(in_features=256, out_features=2048, bias=True)
          (fc2): Linear(in_features=2048, out_features=256, bias=True)
          (final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        )
      )
    )
    (decoder): DetrDecoder(
      (layers): ModuleList(
        (0-5): 6 x DetrDecoderLayer(
          (self_attn): DetrAttention(
            (k_proj): Linear(in_features=256, out_features=256, bias=True)
            (v_proj): Linear(in_features=256, out_features=256, bias=True)
            (q_proj): Linear(in_features=256, out_features=256, bias=True)
            (out_proj): Linear(in_features=256, out_features=256, bias=True)
          )
          (activation_fn): ReLU()
          (self_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): DetrAttention(
            (k_proj): Linear(in_features=256, out_features=256, bias=True)
            (v_proj): Linear(in_features=256, out_features=256, bias=True)
            (q_proj): Linear(in_features=256, out_features=256, bias=True)
            (out_proj): Linear(in_features=256, out_features=256, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=256, out_features=2048, bias=True)
          (fc2): Linear(in_features=2048, out_features=256, bias=True)
          (final_layer_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layernorm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
    )
  )
  (class_labels_classifier): Linear(in_features=256, out_features=2, bias=True)
  (bbox_predictor): DetrMLPPredictionHead(
    (layers): ModuleList(
      (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)
      (2): Linear(in_features=256, out_features=4, bias=True)
    )
  )
)"""
}