{"record":{"id":"933543ba2ef6e9bd","repo":"lllyasviel/style2paints","slug":"no-input-variables-found-for-layer-s","errorCode":null,"errorMessage":"No input variables found for layer %s.","messagePattern":"No input variables found for layer (.+?)\\.","errorType":"exception","errorClass":"RuntimeError","httpStatus":null,"severity":"error","filePath":"V4/s2p_v4_server/smoother.py","lineNumber":15,"sourceCode":"import numpy as np\nimport scipy.stats as st\nimport tensorflow\n\ntensorflow.compat.v1.disable_v2_behavior()\ntf = tensorflow.compat.v1\n\n\ndef layer(op):\n    def layer_decorated(self, *args, **kwargs):\n        # Automatically set a name if not provided.\n        name = kwargs.setdefault('name', self.get_unique_name(op.__name__))\n        # Figure out the layer inputs.\n        if len(self.terminals) == 0:\n            raise RuntimeError('No input variables found for layer %s.' % name)\n        elif len(self.terminals) == 1:\n            layer_input = self.terminals[0]\n        else:\n            layer_input = list(self.terminals)\n        # Perform the operation and get the output.\n        layer_output = op(self, layer_input, *args, **kwargs)\n        # Add to layer LUT.\n        self.layers[name] = layer_output\n        # This output is now the input for the next layer.\n        self.feed(layer_output)\n        # Return self for chained calls.\n        return self\n\n    return layer_decorated\n\n\nclass Smoother(object):\n    def __init__(self, inputs, filter_size, sigma):","sourceCodeStart":1,"sourceCodeEnd":33,"githubUrl":"https://github.com/lllyasviel/style2paints/blob/a0d164d6a8a69fa4a87139bcd193291057f4ca39/V4/s2p_v4_server/smoother.py#L1-L33","documentation":"The @layer decorator wraps every network-building op; before invoking the op it reads self.terminals, the stack of current output variables of the Network. If terminals is empty there is no input to feed the new layer, so it raises immediately. This enforces that you always seed the graph (via feed or an input-producing op) before chaining layers.","triggerScenarios":"Calling any decorated layer method (conv, max_pool, etc.) as the FIRST operation on a freshly constructed Network, before calling feed() or any op that sets terminals — e.g. `net = Network(); net.conv(...)` with no prior feed/input.","commonSituations":"Forgetting the initial feed() call after constructing the network; constructing a second Network object and reusing a layer-call sequence copied from code that had a feed; a branch where a conditional op never populated terminals.","solutions":["Call net.feed(input_tensor_or_layer_name) (or the setup/input op) before the first layer call","Ensure the previous layer call actually succeeded — a swallowed exception can leave terminals empty","If building multiple subgraphs, feed the correct starting layer before each new op chain"],"exampleFix":"# before\nnet = Network()\nnet.conv(3, 32, name='conv1')  # RuntimeError\n\n# after\nnet = Network()\nnet.feed(image_placeholder, name='input')\nnet.conv(3, 32, name='conv1')","handlingStrategy":"try-catch","validationCode":"if len(net.terminals) == 0:\n    net.feed(input_tensor)  # seed graph before layer calls","typeGuard":null,"tryCatchPattern":"try:\n    net.conv(3, 32, name='conv1')\nexcept RuntimeError as e:\n    if 'No input variables found' in str(e):\n        net.feed(input_tensor)\n        net.conv(3, 32, name='conv1')\n    else:\n        raise","preventionTips":["Always call feed()/setup() immediately after constructing the Network","Never interleave layer calls across Network instances","Wrap multi-step build sequences so a failure mid-chain doesn't leave terminals empty unnoticed"],"tags":["tensorflow","network-builder","runtimeerror","graph-construction"],"backgroundTag":"no-input-variable-for-layer","analyzedSha":"a0d164d6a8a69fa4a87139bcd193291057f4ca39","analyzedAt":"2026-09-02T22:11:10.837Z","contentChangedAt":null,"schemaVersion":2},"datasetVersion":"2026-09-10T02:17:09.455Z"}