From a4be67e5889de719d07085450cf8344d58a78f1c Mon Sep 17 00:00:00 2001 From: Ignacio Oguiza <11656416+oguiza@users.noreply.github.com> Date: Wed, 31 Jan 2024 13:30:51 +0100 Subject: [PATCH] bumped tsai version --- nbs/030_models.utils.ipynb | 76 ++++++++++++++++++------------------- nbs/models/test.pth | Bin 1100072 -> 1100008 bytes settings.ini | 6 +-- tsai/__init__.py | 2 +- tsai/models/utils.py | 56 +++++++++++++-------------- 5 files changed, 70 insertions(+), 70 deletions(-) diff --git a/nbs/030_models.utils.ipynb b/nbs/030_models.utils.ipynb index b8f737043..d17d36757 100644 --- a/nbs/030_models.utils.ipynb +++ b/nbs/030_models.utils.ipynb @@ -66,7 +66,7 @@ "#|export\n", "def SeqTokenizer(c_in, embed_dim, token_size=60, norm=False):\n", " \"Generates non-overlapping tokens from sub-sequences within a sequence by applying a sliding window\"\n", - " return ConvBlock(c_in, embed_dim, token_size, stride=token_size, padding=0, act=None, \n", + " return ConvBlock(c_in, embed_dim, token_size, stride=token_size, padding=0, act=None,\n", " norm='Batch' if norm else None, bias=norm is None)\n", "\n", "SeqEmbed = SeqTokenizer" @@ -149,18 +149,18 @@ "#|export\n", "def check_bias(m, cond=noop, verbose=False):\n", " mean, std = [], []\n", - " for i,l in enumerate(get_layers(m, cond=cond)): \n", - " if hasattr(l, 'bias') and l.bias is not None: \n", + " for i,l in enumerate(get_layers(m, cond=cond)):\n", + " if hasattr(l, 'bias') and l.bias is not None:\n", " b = l.bias.data\n", " mean.append(b.mean())\n", " std.append(b.std())\n", " pv(f'{i:3} {l.__class__.__name__:15} shape: {str(list(b.shape)):15} mean: {b.mean():+6.3f} std: {b.std():+6.3f}', verbose)\n", " return np.array(mean), np.array(std)\n", - " \n", + "\n", "def check_weight(m, cond=noop, verbose=False):\n", " mean, std = [], []\n", - " for i,l in enumerate(get_layers(m, cond=cond)): \n", - " if hasattr(l, 'weight') and l.weight is not None: \n", + " for i,l in enumerate(get_layers(m, cond=cond)):\n", + " if hasattr(l, 'weight') and l.weight is not None:\n", " w = l.weight.data\n", " mean.append(w.mean())\n", " std.append(w.std())\n", @@ -234,10 +234,10 @@ "outputs": [], "source": [ "#|export\n", - "def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, device=None, verbose=False, \n", - " s_cat_idxs=None, s_cat_embeddings=None, s_cat_embedding_dims=None, s_cont_idxs=None, \n", + "def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, device=None, verbose=False,\n", + " s_cat_idxs=None, s_cat_embeddings=None, s_cat_embedding_dims=None, s_cont_idxs=None,\n", " o_cat_idxs=None, o_cat_embeddings=None, o_cat_embedding_dims=None, o_cont_idxs=None,\n", - " patch_len=None, patch_stride=None, fusion_layers=128, fusion_act='relu', fusion_dropout=0., fusion_use_bn=True, \n", + " patch_len=None, patch_stride=None, fusion_layers=128, fusion_act='relu', fusion_dropout=0., fusion_use_bn=True,\n", " pretrained=False, weights_path=None, exclude_head=True, cut=-1, init=None, arch_config={}, **kwargs):\n", "\n", " device = ifnone(device, default_device())\n", @@ -246,20 +246,20 @@ " c_out = ifnone(c_out, dls.c)\n", " seq_len = ifnone(seq_len, dls.len)\n", " d = ifnone(d, dls.d)\n", - " \n", + "\n", " if s_cat_idxs or s_cat_embeddings or s_cat_embedding_dims or s_cont_idxs or o_cat_idxs or o_cat_embeddings or o_cat_embedding_dims or o_cont_idxs:\n", " from tsai.models.multimodal import MultInputWrapper\n", " model = MultInputWrapper(arch, c_in=c_in, c_out=c_out, seq_len=seq_len, d=d,\n", - " s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims, s_cont_idxs=s_cont_idxs, \n", - " o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims, o_cont_idxs=o_cont_idxs, \n", - " patch_len=patch_len, patch_stride=patch_stride, \n", - " fusion_layers=fusion_layers, fusion_act=fusion_act, fusion_dropout=fusion_dropout, fusion_use_bn=fusion_use_bn,\n", + " s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims, s_cont_idxs=s_cont_idxs,\n", + " o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims, o_cont_idxs=o_cont_idxs,\n", + " patch_len=patch_len, patch_stride=patch_stride,\n", + " fusion_layers=fusion_layers, fusion_act=fusion_act, fusion_dropout=fusion_dropout, fusion_use_bn=fusion_use_bn,**arch_config,\n", " **kwargs)\n", " else:\n", - " if d and arch.__name__ not in [\"PatchTST\", \"PatchTSTPlus\", 'TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', \n", - " \"RNN_FCNPlus\", \"LSTM_FCNPlus\", \"GRU_FCNPlus\", \"MRNN_FCNPlus\", \"MLSTM_FCNPlus\", \"MGRU_FCNPlus\", \n", + " if d and arch.__name__ not in [\"PatchTST\", \"PatchTSTPlus\", 'TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus',\n", + " \"RNN_FCNPlus\", \"LSTM_FCNPlus\", \"GRU_FCNPlus\", \"MRNN_FCNPlus\", \"MLSTM_FCNPlus\", \"MGRU_FCNPlus\",\n", " \"RNNAttentionPlus\", \"LSTMAttentionPlus\", \"GRUAttentionPlus\", \"ConvTran\", \"ConvTranPlus\"]:\n", - " if 'custom_head' not in kwargs.keys(): \n", + " if 'custom_head' not in kwargs.keys():\n", " if \"rocket\" in arch.__name__.lower() or 'hydra' in arch.__name__.lower():\n", " kwargs['custom_head'] = partial(rocket_nd_head, d=d)\n", " elif \"xresnet1d\" in arch.__name__.lower():\n", @@ -271,13 +271,13 @@ " if 'ltsf_' in arch.__name__.lower() or 'patchtst' in arch.__name__.lower():\n", " pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} pred_dim={d} arch_config={arch_config}, kwargs={kwargs})', verbose)\n", " model = (arch(c_in=c_in, c_out=c_out, seq_len=seq_len, pred_dim=d, **arch_config, **kwargs)).to(device=device)\n", - " elif arch.__name__ in ['TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', \"RNN_FCNPlus\", \"LSTM_FCNPlus\", \"GRU_FCNPlus\", \"MRNN_FCNPlus\", \n", + " elif arch.__name__ in ['TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', \"RNN_FCNPlus\", \"LSTM_FCNPlus\", \"GRU_FCNPlus\", \"MRNN_FCNPlus\",\n", " \"MLSTM_FCNPlus\", \"MGRU_FCNPlus\", \"RNNAttentionPlus\", \"LSTMAttentionPlus\", \"GRUAttentionPlus\", \"ConvTran\", \"ConvTranPlus\", 'mWDNPlus']:\n", " pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} d={d} arch_config={arch_config}, kwargs={kwargs})', verbose)\n", " model = (arch(c_in=c_in, c_out=c_out, seq_len=seq_len, d=d, **arch_config, **kwargs)).to(device=device)\n", " elif sum([1 for v in ['RNN_FCN', 'LSTM_FCN', 'RNNPlus', 'LSTMPlus', 'GRUPlus', 'InceptionTime', 'TSiT', 'Sequencer', 'XceptionTimePlus',\n", - " 'GRU_FCN', 'OmniScaleCNN', 'mWDN', 'TST', 'XCM', 'MLP', 'MiniRocket', 'InceptionRocket', 'ResNetPlus', \n", - " 'RNNAttention', 'LSTMAttention', 'GRUAttention', 'MultiRocket', 'MultiRocketPlus', 'Hydra', 'HydraPlus', \n", + " 'GRU_FCN', 'OmniScaleCNN', 'mWDN', 'TST', 'XCM', 'MLP', 'MiniRocket', 'InceptionRocket', 'ResNetPlus',\n", + " 'RNNAttention', 'LSTMAttention', 'GRUAttention', 'MultiRocket', 'MultiRocketPlus', 'Hydra', 'HydraPlus',\n", " 'HydraMultiRocket', 'HydraMultiRocketPlus']\n", " if v in arch.__name__]):\n", " pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} arch_config={arch_config} kwargs={kwargs})', verbose)\n", @@ -324,7 +324,7 @@ " setattr(model, \"__name__\", arch.__name__)\n", "\n", " return model\n", - " \n", + "\n", "build_model = build_ts_model\n", "create_model = build_ts_model" ] @@ -354,10 +354,10 @@ " if dls is not None:\n", " c_in = ifnone(c_in, dls.vars)\n", " c_out = ifnone(c_out, dls.c)\n", - " \n", + "\n", " model = arch(pretrained=pretrained, c_in=c_in, n_out=c_out, **arch_config, **kwargs).to(device=device)\n", " setattr(model, \"__name__\", arch.__name__)\n", - " if init is not None: \n", + " if init is not None:\n", " apply_init(model[1] if pretrained else model, init)\n", " return model" ] @@ -378,7 +378,7 @@ " assert n_out, \"`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`\"\n", " if y_range is None and 'y_range' in kwargs: y_range = kwargs.pop('y_range')\n", " model = arch(emb_szs, len(dls.cont_names), n_out, layers, y_range=y_range, **arch_config, **kwargs).to(device=device)\n", - " \n", + "\n", " if hasattr(model, \"head_nf\"): head_nf = model.head_nf\n", " else: head_nf = get_nf(model)\n", " setattr(model, \"__name__\", arch.__name__)\n", @@ -472,10 +472,10 @@ " if seq_len is None:\n", " seq_len = 50\n", " return_q_len = False\n", - " try: \n", + " try:\n", " params_0 = list(mod.parameters())[0]\n", " xb = torch.rand(1, c_in, seq_len, device=params_0.device, dtype=params_0.dtype)\n", - " except: \n", + " except:\n", " xb = torch.rand(1, c_in, seq_len)\n", " training = mod.training\n", " mod.eval()\n", @@ -487,7 +487,7 @@ " mod.training = training\n", " if return_q_len:\n", " return c_out, q_len\n", - " else: \n", + " else:\n", " return c_out, None" ] }, @@ -542,7 +542,7 @@ "\n", "def true_forecaster(o, split, horizon=1):\n", " o_true = o[split]\n", - " if is_listy(horizon): \n", + " if is_listy(horizon):\n", " o_true = o_true[np.newaxis].repeat(len(horizon), 0)\n", " return o_true" ] @@ -555,14 +555,14 @@ { "data": { "text/plain": [ - "(array([0.35514714, 0.79371588, 0.89324833, 1.78069172, 2.26929887,\n", - " 2.8927336 , 2.93275611, 3.693081 , 4.34535574, 5.22502166,\n", - " 5.42378301, 5.57022079, 5.61515208, 5.66301663, 5.87813728,\n", - " 6.26954548, 7.12932702, 7.40652216, 7.58915979, 8.19573115]),\n", - " array([5.22502166, 5.42378301, 5.57022079, 5.61515208, 5.66301663,\n", - " 5.87813728, 6.26954548, 7.12932702, 7.40652216, 7.58915979]),\n", - " array([5.42378301, 5.57022079, 5.61515208, 5.66301663, 5.87813728,\n", - " 6.26954548, 7.12932702, 7.40652216, 7.58915979, 8.19573115]))" + "(array([ 0.74775537, 1.41245663, 2.12445924, 2.8943163 , 3.56384351,\n", + " 4.23789602, 4.83134182, 5.18560431, 5.30551186, 6.29076506,\n", + " 6.58873471, 7.03661275, 7.0884361 , 7.57927022, 8.21911791,\n", + " 8.59726773, 9.37382718, 10.17298849, 10.40118308, 10.82265631]),\n", + " array([ 6.29076506, 6.58873471, 7.03661275, 7.0884361 , 7.57927022,\n", + " 8.21911791, 8.59726773, 9.37382718, 10.17298849, 10.40118308]),\n", + " array([ 6.58873471, 7.03661275, 7.0884361 , 7.57927022, 8.21911791,\n", + " 8.59726773, 9.37382718, 10.17298849, 10.40118308, 10.82265631]))" ] }, "execution_count": null, @@ -595,9 +595,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "/Users/nacho/notebooks/tsai/nbs/030_models.utils.ipynb saved at 2023-07-04 17:34:15\n", + "/Users/nacho/notebooks/tsai/nbs/030_models.utils.ipynb saved at 2024-01-31 13:03:06\n", "Correct notebook to script conversion! 😃\n", - "Tuesday 04/07/23 17:34:17 CEST\n" + "Wednesday 31/01/24 13:03:08 CET\n" ] }, { diff --git a/nbs/models/test.pth b/nbs/models/test.pth index ec0e1cdcea85cf9406570a4c482e8c23beb2399d..1bfd7aa152705a6d88aa61f38fa40969b5e02dcf 100644 GIT binary patch literal 1100008 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d4370829a..eb8be1a40 100644 --- a/settings.ini +++ b/settings.ini @@ -4,7 +4,7 @@ lib_name = tsai repo = tsai user = timeseriesAI branch = main -version = 0.3.8 +version = 0.3.9 description = Practical Deep Learning for Time Series / Sequential Data library based on fastai & Pytorch keywords = fastai time-series time-series-classification time-series-regression deep-learning Pytorch author = Ignacio Oguiza and contributors @@ -35,8 +35,8 @@ recursive = True clean_ids = True black_formatting = False readme_nb = index.ipynb -allowed_metadata_keys = -allowed_cell_metadata_keys = +allowed_metadata_keys = +allowed_cell_metadata_keys = jupyter_hooks = True clear_all = False put_version_in_init = True diff --git a/tsai/__init__.py b/tsai/__init__.py index b93693a3c..b0f2ddc27 100644 --- a/tsai/__init__.py +++ b/tsai/__init__.py @@ -1 +1 @@ -__version__ = "0.3.8" \ No newline at end of file +__version__ = "0.3.9" \ No newline at end of file diff --git a/tsai/models/utils.py b/tsai/models/utils.py index dc65d5cc6..90fd7274d 100644 --- a/tsai/models/utils.py +++ b/tsai/models/utils.py @@ -29,7 +29,7 @@ def apply_idxs(o, idxs): # %% ../../nbs/030_models.utils.ipynb 5 def SeqTokenizer(c_in, embed_dim, token_size=60, norm=False): "Generates non-overlapping tokens from sub-sequences within a sequence by applying a sliding window" - return ConvBlock(c_in, embed_dim, token_size, stride=token_size, padding=0, act=None, + return ConvBlock(c_in, embed_dim, token_size, stride=token_size, padding=0, act=None, norm='Batch' if norm else None, bias=norm is None) SeqEmbed = SeqTokenizer @@ -82,18 +82,18 @@ def has_weight_or_bias(l): # %% ../../nbs/030_models.utils.ipynb 9 def check_bias(m, cond=noop, verbose=False): mean, std = [], [] - for i,l in enumerate(get_layers(m, cond=cond)): - if hasattr(l, 'bias') and l.bias is not None: + for i,l in enumerate(get_layers(m, cond=cond)): + if hasattr(l, 'bias') and l.bias is not None: b = l.bias.data mean.append(b.mean()) std.append(b.std()) pv(f'{i:3} {l.__class__.__name__:15} shape: {str(list(b.shape)):15} mean: {b.mean():+6.3f} std: {b.std():+6.3f}', verbose) return np.array(mean), np.array(std) - + def check_weight(m, cond=noop, verbose=False): mean, std = [], [] - for i,l in enumerate(get_layers(m, cond=cond)): - if hasattr(l, 'weight') and l.weight is not None: + for i,l in enumerate(get_layers(m, cond=cond)): + if hasattr(l, 'weight') and l.weight is not None: w = l.weight.data mean.append(w.mean()) std.append(w.std()) @@ -139,10 +139,10 @@ def transfer_weights(model, weights_path:Path, device:torch.device=None, exclude print(f"weights from {weights_path} successfully transferred!\n") # %% ../../nbs/030_models.utils.ipynb 13 -def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, device=None, verbose=False, - s_cat_idxs=None, s_cat_embeddings=None, s_cat_embedding_dims=None, s_cont_idxs=None, +def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, device=None, verbose=False, + s_cat_idxs=None, s_cat_embeddings=None, s_cat_embedding_dims=None, s_cont_idxs=None, o_cat_idxs=None, o_cat_embeddings=None, o_cat_embedding_dims=None, o_cont_idxs=None, - patch_len=None, patch_stride=None, fusion_layers=128, fusion_act='relu', fusion_dropout=0., fusion_use_bn=True, + patch_len=None, patch_stride=None, fusion_layers=128, fusion_act='relu', fusion_dropout=0., fusion_use_bn=True, pretrained=False, weights_path=None, exclude_head=True, cut=-1, init=None, arch_config={}, **kwargs): device = ifnone(device, default_device()) @@ -151,20 +151,20 @@ def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, c_out = ifnone(c_out, dls.c) seq_len = ifnone(seq_len, dls.len) d = ifnone(d, dls.d) - + if s_cat_idxs or s_cat_embeddings or s_cat_embedding_dims or s_cont_idxs or o_cat_idxs or o_cat_embeddings or o_cat_embedding_dims or o_cont_idxs: from tsai.models.multimodal import MultInputWrapper model = MultInputWrapper(arch, c_in=c_in, c_out=c_out, seq_len=seq_len, d=d, - s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims, s_cont_idxs=s_cont_idxs, - o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims, o_cont_idxs=o_cont_idxs, - patch_len=patch_len, patch_stride=patch_stride, - fusion_layers=fusion_layers, fusion_act=fusion_act, fusion_dropout=fusion_dropout, fusion_use_bn=fusion_use_bn, + s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims, s_cont_idxs=s_cont_idxs, + o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims, o_cont_idxs=o_cont_idxs, + patch_len=patch_len, patch_stride=patch_stride, + fusion_layers=fusion_layers, fusion_act=fusion_act, fusion_dropout=fusion_dropout, fusion_use_bn=fusion_use_bn,**arch_config, **kwargs) else: - if d and arch.__name__ not in ["PatchTST", "PatchTSTPlus", 'TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', - "RNN_FCNPlus", "LSTM_FCNPlus", "GRU_FCNPlus", "MRNN_FCNPlus", "MLSTM_FCNPlus", "MGRU_FCNPlus", + if d and arch.__name__ not in ["PatchTST", "PatchTSTPlus", 'TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', + "RNN_FCNPlus", "LSTM_FCNPlus", "GRU_FCNPlus", "MRNN_FCNPlus", "MLSTM_FCNPlus", "MGRU_FCNPlus", "RNNAttentionPlus", "LSTMAttentionPlus", "GRUAttentionPlus", "ConvTran", "ConvTranPlus"]: - if 'custom_head' not in kwargs.keys(): + if 'custom_head' not in kwargs.keys(): if "rocket" in arch.__name__.lower() or 'hydra' in arch.__name__.lower(): kwargs['custom_head'] = partial(rocket_nd_head, d=d) elif "xresnet1d" in arch.__name__.lower(): @@ -176,13 +176,13 @@ def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, if 'ltsf_' in arch.__name__.lower() or 'patchtst' in arch.__name__.lower(): pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} pred_dim={d} arch_config={arch_config}, kwargs={kwargs})', verbose) model = (arch(c_in=c_in, c_out=c_out, seq_len=seq_len, pred_dim=d, **arch_config, **kwargs)).to(device=device) - elif arch.__name__ in ['TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', "RNN_FCNPlus", "LSTM_FCNPlus", "GRU_FCNPlus", "MRNN_FCNPlus", + elif arch.__name__ in ['TransformerRNNPlus', 'TransformerLSTMPlus', 'TransformerGRUPlus', "RNN_FCNPlus", "LSTM_FCNPlus", "GRU_FCNPlus", "MRNN_FCNPlus", "MLSTM_FCNPlus", "MGRU_FCNPlus", "RNNAttentionPlus", "LSTMAttentionPlus", "GRUAttentionPlus", "ConvTran", "ConvTranPlus", 'mWDNPlus']: pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} d={d} arch_config={arch_config}, kwargs={kwargs})', verbose) model = (arch(c_in=c_in, c_out=c_out, seq_len=seq_len, d=d, **arch_config, **kwargs)).to(device=device) elif sum([1 for v in ['RNN_FCN', 'LSTM_FCN', 'RNNPlus', 'LSTMPlus', 'GRUPlus', 'InceptionTime', 'TSiT', 'Sequencer', 'XceptionTimePlus', - 'GRU_FCN', 'OmniScaleCNN', 'mWDN', 'TST', 'XCM', 'MLP', 'MiniRocket', 'InceptionRocket', 'ResNetPlus', - 'RNNAttention', 'LSTMAttention', 'GRUAttention', 'MultiRocket', 'MultiRocketPlus', 'Hydra', 'HydraPlus', + 'GRU_FCN', 'OmniScaleCNN', 'mWDN', 'TST', 'XCM', 'MLP', 'MiniRocket', 'InceptionRocket', 'ResNetPlus', + 'RNNAttention', 'LSTMAttention', 'GRUAttention', 'MultiRocket', 'MultiRocketPlus', 'Hydra', 'HydraPlus', 'HydraMultiRocket', 'HydraMultiRocketPlus'] if v in arch.__name__]): pv(f'arch: {arch.__name__}(c_in={c_in} c_out={c_out} seq_len={seq_len} arch_config={arch_config} kwargs={kwargs})', verbose) @@ -229,7 +229,7 @@ def build_ts_model(arch, c_in=None, c_out=None, seq_len=None, d=None, dls=None, setattr(model, "__name__", arch.__name__) return model - + build_model = build_ts_model create_model = build_ts_model @@ -245,10 +245,10 @@ def build_tsimage_model(arch, c_in=None, c_out=None, dls=None, pretrained=False, if dls is not None: c_in = ifnone(c_in, dls.vars) c_out = ifnone(c_out, dls.c) - + model = arch(pretrained=pretrained, c_in=c_in, n_out=c_out, **arch_config, **kwargs).to(device=device) setattr(model, "__name__", arch.__name__) - if init is not None: + if init is not None: apply_init(model[1] if pretrained else model, init) return model @@ -262,7 +262,7 @@ def build_tabular_model(arch, dls, layers=None, emb_szs=None, n_out=None, y_rang assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`" if y_range is None and 'y_range' in kwargs: y_range = kwargs.pop('y_range') model = arch(emb_szs, len(dls.cont_names), n_out, layers, y_range=y_range, **arch_config, **kwargs).to(device=device) - + if hasattr(model, "head_nf"): head_nf = model.head_nf else: head_nf = get_nf(model) setattr(model, "__name__", arch.__name__) @@ -286,10 +286,10 @@ def output_size_calculator(mod, c_in, seq_len=None): if seq_len is None: seq_len = 50 return_q_len = False - try: + try: params_0 = list(mod.parameters())[0] xb = torch.rand(1, c_in, seq_len, device=params_0.device, dtype=params_0.dtype) - except: + except: xb = torch.rand(1, c_in, seq_len) training = mod.training mod.eval() @@ -301,7 +301,7 @@ def output_size_calculator(mod, c_in, seq_len=None): mod.training = training if return_q_len: return c_out, q_len - else: + else: return c_out, None # %% ../../nbs/030_models.utils.ipynb 24 @@ -321,6 +321,6 @@ def naive_forecaster(o, split, horizon=1): def true_forecaster(o, split, horizon=1): o_true = o[split] - if is_listy(horizon): + if is_listy(horizon): o_true = o_true[np.newaxis].repeat(len(horizon), 0) return o_true

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