vicellst-official

Runtime error

App Files Files Community

vicellst-official / constants.py

polejowska

Update constants.py

a46c0cf 9 months ago

raw history blame contribute delete

No virus

14.9 kB

	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)
	)
	)
	)"""
	}