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| """This code is taken from <https://github.com/alexandre01/deepsvg> | |
| by Alexandre Carlier, Martin Danelljan, Alexandre Alahi and Radu Timofte | |
| from the paper >https://arxiv.org/pdf/2007.11301.pdf> | |
| """ | |
| from src.preprocessing.deepsvg.deepsvg_difflib.tensor import SVGTensor | |
| class _DefaultConfig: | |
| """ | |
| Model config. | |
| """ | |
| def __init__(self): | |
| self.args_dim = 256 # Coordinate numericalization, default: 256 (8-bit) | |
| self.n_args = 11 # Tensor nb of arguments, default: 11 (rx,ry,phi,fA,fS,qx1,qy1,qx2,qy2,x1,x2) | |
| self.n_commands = len(SVGTensor.COMMANDS_SIMPLIFIED) # m, l, c, a, EOS, SOS, z | |
| self.dropout = 0.1 # Dropout rate used in basic layers and Transformers | |
| self.model_type = "transformer" # "transformer" ("lstm" implementation is work in progress) | |
| self.encode_stages = 1 # One-stage or two-stage: 1 | 2 | |
| self.decode_stages = 1 # One-stage or two-stage: 1 | 2 | |
| self.use_resnet = True # Use extra fully-connected residual blocks after Encoder | |
| self.use_vae = True # Sample latent vector (with reparametrization trick) or use encodings directly | |
| self.pred_mode = "one_shot" # Feed-forward (one-shot) or autogressive: "one_shot" | "autoregressive" | |
| self.rel_targets = False # Predict coordinates in relative or absolute format | |
| self.label_condition = False # Make all blocks conditional on the label | |
| self.n_labels = 100 # Number of labels (when used) | |
| self.dim_label = 64 # Label embedding dimensionality | |
| self.self_match = False # Use Hungarian (self-match) or Ordered assignment | |
| self.n_layers = 4 # Number of Encoder blocks | |
| self.n_layers_decode = 4 # Number of Decoder blocks | |
| self.n_heads = 8 # Transformer config: number of heads | |
| self.dim_feedforward = 512 # Transformer config: FF dimensionality | |
| self.d_model = 256 # Transformer config: model dimensionality | |
| self.dim_z = 256 # Latent vector dimensionality | |
| self.max_num_groups = 8 # Number of paths (N_P) | |
| self.max_seq_len = 30 # Number of commands (N_C) | |
| self.max_total_len = self.max_num_groups * self.max_seq_len # Concatenated sequence length for baselines | |
| self.num_groups_proposal = self.max_num_groups # Number of predicted paths, default: N_P | |
| def get_model_args(self): | |
| model_args = [] | |
| model_args += ["commands_grouped", "args_grouped"] if self.encode_stages <= 1 else ["commands", "args"] | |
| if self.rel_targets: | |
| model_args += ["commands_grouped", "args_rel_grouped"] if self.decode_stages == 1 else ["commands", "args_rel"] | |
| else: | |
| model_args += ["commands_grouped", "args_grouped"] if self.decode_stages == 1 else ["commands", "args"] | |
| if self.label_condition: | |
| model_args.append("label") | |
| return model_args | |
| class SketchRNN(_DefaultConfig): | |
| # LSTM - Autoregressive - One-stage | |
| def __init__(self): | |
| super().__init__() | |
| self.model_type = "lstm" | |
| self.pred_mode = "autoregressive" | |
| self.rel_targets = True | |
| class Sketchformer(_DefaultConfig): | |
| # Transformer - Autoregressive - One-stage | |
| def __init__(self): | |
| super().__init__() | |
| self.pred_mode = "autoregressive" | |
| self.rel_targets = True | |
| class OneStageOneShot(_DefaultConfig): | |
| # Transformer - One-shot - One-stage | |
| def __init__(self): | |
| super().__init__() | |
| self.encode_stages = 1 | |
| self.decode_stages = 1 | |
| class Hierarchical(_DefaultConfig): | |
| # Transformer - One-shot - Two-stage - Ordered | |
| def __init__(self): | |
| super().__init__() | |
| self.encode_stages = 2 | |
| self.decode_stages = 2 | |
| class HierarchicalSelfMatching(_DefaultConfig): | |
| # Transformer - One-shot - Two-stage - Hungarian | |
| def __init__(self): | |
| super().__init__() | |
| self.encode_stages = 2 | |
| self.decode_stages = 2 | |
| self.self_match = True | |