[DOCs][Added] Some random notes
This commit is contained in:
Salvador E. Tropea
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informal
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### Takeaway: Findings on `BatchNormalization` Conversion
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Our rigorous, isolated testing of the `BatchNormalization` node has yielded one crucial, definitive finding:
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**A standard PyTorch `torch.nn.BatchNorm2d` layer is a numerically exact equivalent of the ONNX `BatchNormalization` operator when applied to 4D tensors, provided the weights are loaded directly from the ONNX initializers.**
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This was proven by the `isolate_and_verify_bn.py` script, where our manual replication (`my_bn`) perfectly matched the output of the ONNX Runtime ground truth.
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This key finding has two critical implications:
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1. **Our Core Understanding is Correct:** We can be 100% confident that `nn.BatchNorm2d` is the correct PyTorch module to use for this operation in our final standalone model.
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2. **The `onnx2pytorch` Discrepancy:** The fact that the `onnx2pytorch` `BatchNormWrapper` produced a different result in the same test proves that it has a non-standard implementation. While this non-standard behavior works within the context of the full `onnx2pytorch` converted model, it is not a faithful reproduction of the ONNX specification for that isolated layer. Therefore, we should **not** use the `onnx2pytorch` `state_dict` as a source for `BatchNorm` weights, as they may be part of a flawed layer; we must use the raw ONNX initializers.
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## **Architectural Analysis of a Hybrid Time-Frequency Domain U-Net for Music Source Separation**
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### Abstract
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This document provides a detailed architectural analysis of a sophisticated deep learning model used for state-of-the-art music source separation. The architecture, a common and high-performing topology found in models submitted to the Music Demixing (MDX) Challenge and used by prominent community trainers like Kim Jensen, is a heavily modified U-Net. Its core innovation is a unique building block that processes audio information in both the time and frequency domains sequentially within a residual connection. This hybrid approach allows the network to effectively learn both the local timbral characteristics and the global harmonic structure of audio, leading to high-fidelity separation of stems like vocals, drums, and bass.
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---
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### 1. Overall Architecture: A Modified U-Net
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At the highest level, the model is a variant of the classic **U-Net architecture**, widely used in both image segmentation and audio processing. The network consists of an encoder path, a bottleneck, and a decoder path, with a total of five resolution levels.
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* **Encoder:** A series of five processing stages (`enc_stage_n`) that progressively extract features while five downsampling layers (`enc_down_n`) reduce the spatial dimensions of the feature maps by a factor of two at each step.
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* **Bottleneck:** The deepest point in the network, where the feature representation is most compressed spatially but has the highest channel count.
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* **Decoder:** A symmetric series of five upsampling layers (`dec_up_n`) and five processing stages (`dec_stage_n`) that reconstruct the feature map back to its original dimensions.
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* **Multiplicative Skip Connections:** A key feature of this U-Net is its use of skip connections. The output of each encoder stage (`s_n`) is passed directly to the corresponding decoder stage. However, instead of being concatenated (as in a standard U-Net), it is combined via **element-wise multiplication**. This acts as a "gate," allowing the detailed features from the encoder to modulate and refine the feature maps being reconstructed by the decoder.
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### 2. The Core Component: The Sequential-Residual TDF Block
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The primary innovation of this architecture lies in its core building block, which we have identified as a **Time-Domain Frequency (TDF) Block** with a **sequential-residual** data flow. This block replaces the simple double-convolution block found in traditional U-Nets.
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The data flow within this block is crucial and was a key finding of our analysis:
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1. An input tensor `x` first passes entirely through a **Time-Domain Branch**. Let the result be `time_out`.
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2. The output of the time branch, `time_out`, is then fed as the input to a **Frequency-Domain Branch**. Let the result be `freq_out`.
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3. The final output of the TDF Block is the sum of these two results: `result = time_out + freq_out`. This forms a residual connection where the frequency branch is learning a residual transformation on top of the time branch's output.
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#### 2.1. Time-Domain Branch
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* **Structure:** This branch consists of a simple sequence of three `Conv2d -> ReLU` blocks.
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* **Implementation:** `nn.Sequential(Conv2d(C,C), ReLU(), Conv2d(C,C), ReLU(), Conv2d(C,C), ReLU())`
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* **Purpose:** The time-domain branch uses standard 2D convolutions with a `3x3` kernel. After the initial `Transpose` operation (see Section 3.1), the input tensor's dimensions are `(Batch, Channels, Time, Freq)`. The convolutions operate across the `Time` and `Freq` dimensions, allowing them to learn localized patterns related to timbre, transients, and the short-term temporal evolution of sounds.
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#### 2.2. Frequency-Domain Branch
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* **Structure:** This branch is responsible for capturing the global harmonic structure of the audio. It consists of a `Linear -> BatchNorm2d -> ReLU -> Linear -> BatchNorm2d -> ReLU` sequence.
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* **Implementation Detail - The `nn.Linear` Layer:** The key to this branch is the use of `nn.Linear` layers on a 4D tensor.
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* A layer defined as `nn.Linear(in_features, out_features)` receives a 4D tensor of shape `(B, C, T, F)`, where `F` matches `in_features`.
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* PyTorch's broadcasting capability means the linear transformation is applied to the last dimension only, producing an output of shape `(B, C, T, H)`, where `H` is `out_features`. No manual reshaping is needed in the `forward` pass.
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* **Crucial Insight:** The weight matrix for this layer, as stored in the ONNX file, must be **transposed** before being loaded into the PyTorch `nn.Linear` layer's `.weight` parameter.
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* **Implementation Detail - The `nn.BatchNorm2d` Layer:**
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* This layer receives the 4D output of the `nn.Linear` layer.
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* A standard `nn.BatchNorm2d(C)` operates on the channel dimension (`C`), normalizing across the `B`, `T`, and `H` dimensions. This is the correct and verified behavior.
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### 3. Key Architectural Details
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#### 3.1. Initial `Transpose` Operation
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Immediately after the initial 1x1 convolution, a `Transpose` operation permutes the tensor dimensions from the standard audio-processing format of `(Batch, Channels, Frequency, Time)` to `(Batch, Channels, Time, Frequency)`. This is a critical transformation that enables the rest of the architecture:
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* It places the `Frequency` dimension last, making it compatible with the broadcasting behavior of the `nn.Linear` layers in the frequency branch.
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* It makes `Time` one of the last two dimensions, allowing the `3x3 Conv2d` layers in the time branch to effectively convolve over local time steps.
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A corresponding `Transpose` exists at the very end of the network to revert the tensor to its original dimension order.
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### 4. Scientific Context and Substantiation
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This architecture is a prime example of the hybrid models that have come to dominate the field of music source separation. It combines the strengths of Convolutional Neural Networks (CNNs) and, conceptually, Transformers.
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* **Hybrid Transformer Networks:** The structure of the TDF block is heavily inspired by and directly comparable to the **KUIELab-MDX-Net**, detailed in the paper "Hybrid Transformer for Music Source Separation". The paper describes a U-Net where each block has a local CNN-based module and a global Transformer-based module. Our `TimeBranch` (with its local convolutional receptive field) is the "local" module, and our `FrequencyBranch` (where `nn.Linear` layers operate across the entire frequency spectrum) is the "global" module, acting as a simplified attention mechanism.
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* **Time-Frequency Domain Processing:** The concept of using distinct operations for the time and frequency axes is a cornerstone of modern audio processing. Papers on models like Demucs and the work on Time-Frequency Convolutions (TFC-TDF) established the principle that CNNs are excellent for learning local features (like timbre), while other structures are needed to capture long-range correlations (like musical harmony), which is exactly what our `FrequencyBranch` achieves.
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**References:**
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_Choi, W., Kim, J., & Chung, J. (2021). KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing. arXiv preprint arXiv:2109.05418._
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_Défossez, A., Usunier, N., Bottou, L., & Bach, F. (2019). Music Source Separation in the Waveform Domain. arXiv preprint arXiv:1911.13254._
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_Choi, W., et al. (2020). LaSAFT: Latent-Source-Attentive Frequency-Time-Transformer for Speech Enhancement. arXiv preprint arXiv:2005.07139._ (While for speech, it details the TFC-TDF concept).
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---
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### Conclusion
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The analyzed model is a highly sophisticated and effective U-Net architecture. Its strength lies in its **sequential-residual TDF block**, which processes the input first through a convolutional time-domain branch and then uses that output as the input for a frequency-domain branch, finally combining them with a residual connection. This, combined with multiplicative skip connections and a crucial initial `Transpose` operation, represents a state-of-the-art design pattern that has been empirically validated by top results in public leaderboards like the MDX Challenge.
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## Technical Brief: Converting ONNX `MatMul` to PyTorch
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### Objective
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This document outlines the definitive, verified methodology for converting a `MatMul` operation from an ONNX graph into an equivalent PyTorch `nn.Module`. This process was determined through a rigorous, iterative testing process comparing the ONNX Runtime, the `onnx2pytorch` conversion library, and manual PyTorch implementations.
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### Background
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During the reverse-engineering of an audio separation model, we encountered significant and persistent discrepancies between the output of the ONNX Runtime and our PyTorch implementations for nodes of type `MatMul`. Simple replications using `torch.matmul` or standard `nn.Linear` layers failed, even though the `onnx2pytorch` library produced a functionally correct end-to-end model. This prompted a deep, first-principles investigation to uncover the correct conversion logic.
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### Key Findings & Correct Implementation
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Through a series of targeted tests, we have proven that the conversion of an ONNX `MatMul` node, where one input is a dynamic activation tensor and the other is a static weight initializer, must be handled as follows:
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**The operation is equivalent to a `torch.nn.Linear` layer, with one critical and non-obvious caveat regarding the weight format.**
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#### 1. The `MatMul` to `nn.Linear` Equivalence
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The ONNX `MatMul` operation, when used with a weight tensor (e.g., in a typical fully-connected layer), is functionally identical to a PyTorch `nn.Linear` layer. Both perform the operation `Y = X @ W.T + b`, where `X` is the input activation and `W` is the weight matrix. PyTorch's `nn.Linear` layer correctly handles broadcasting over higher dimensions (e.g., `(Batch, Channels, Time, InFeatures)`) by applying the matrix multiplication only to the last dimension.
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#### 2. The Crucial Insight: The Weight Transposition
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The core of the conversion problem lies in how the weight tensor is stored in the ONNX file versus how it is stored internally by a PyTorch `nn.Linear` layer.
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* A PyTorch `nn.Linear(in_features, out_features)` layer stores its weight parameter as a matrix of shape `(out_features, in_features)`.
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* The `onnx2pytorch` converter, and our subsequent verification tests, have definitively proven that the weight initializers associated with the `MatMul` nodes in our target ONNX file are stored in the shape `(in_features, out_features)`.
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Therefore, the **definitive rule for converting an ONNX `MatMul` node to a PyTorch `nn.Linear` layer is:**
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> 1. Create an `nn.Linear` layer with the correct `in_features` and `out_features`.
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> 2. Load the corresponding raw weight tensor from the ONNX initializer.
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> 3. **You MUST transpose (`.t()`) this raw weight tensor before copying it into the `.weight.data` parameter of the `nn.Linear` layer.**
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This is demonstrated by the following verified code snippet:
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```python
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# Assume raw_weight_W is a numpy array from the ONNX initializer
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# with shape (in_features, out_features), e.g., (3072, 384)
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in_features = raw_weight_W.shape[0]
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out_features = raw_weight_W.shape[1]
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# 1. Create the PyTorch layer
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my_linear_layer = nn.Linear(in_features, out_features, bias=False)
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# 2. Load the raw weight and convert to a tensor
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raw_weight_tensor = torch.from_numpy(raw_weight_W)
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# 3. Transpose the raw weight and copy it into the layer
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# raw_weight_tensor.t() has shape (out_features, in_features),
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# which matches the internal shape of my_linear_layer.weight
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my_linear_layer.weight.data.copy_(raw_weight_tensor.t())
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```
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### Conclusions for Future Conversions
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1. **Trust but Verify:** The `__str__` representation of a converted model (e.g., from `onnx2pytorch`) can be misleading. While it may print `<class 'torch.nn.modules.linear.Linear'>`, this does not reveal the crucial weight transformations that happened during its initialization.
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2. **The Transpose is Key:** The primary source of error when converting `MatMul` to `nn.Linear` is failing to account for the potential mismatch in weight storage conventions. Always assume the raw ONNX weight needs to be transposed.
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3. **Broadcasting Works:** Do not manually `reshape` or `flatten` a multi-dimensional tensor (e.g., `(B, C, T, F)`) before passing it to an `nn.Linear` layer. The layer's `forward` pass correctly handles broadcasting and operates on the last dimension only. Manually reshaping is a common source of bugs.
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4. **Isolate and Test:** When a complex model fails, the most effective debugging strategy is to create a minimal, self-contained ONNX file for the single problematic layer and test it against the ONNX Runtime, which serves as the ultimate ground truth.
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By adhering to these principles, particularly the mandatory transposition of the raw weight initializer, a successful and numerically accurate conversion from an ONNX `MatMul` node to a PyTorch `nn.Linear` layer can be reliably achieved.
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### Summary: Converting ONNX `MatMul` to PyTorch
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After a comprehensive, iterative debugging process, we have definitively determined the correct methodology for converting an ONNX `MatMul` node (used as a dense layer) into its equivalent PyTorch `nn.Module`.
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**The Key Finding:**
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The core of the conversion lies in resolving a mismatch between how weights are stored. The ONNX `MatMul` operation uses a weight tensor with a shape of `(in_features, out_features)`. In contrast, PyTorch's `nn.Linear` layer stores its internal weight parameter with the shape `(out_features, in_features)`.
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Therefore, a direct copy is impossible. The solution, proven through our tests, is that **the raw weight tensor from the ONNX file must be transposed before being loaded into the PyTorch `nn.Linear` layer.**
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**The Correct, Verified Procedure:**
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1. **Identify the Node:** For an ONNX `MatMul` node with an activation input `A` and a weight initializer `W`.
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2. **Determine Layer Shape:** Inspect the shape of the raw weight tensor `W`. Its shape `(K, M)` corresponds to `(in_features, out_features)`.
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3. **Create PyTorch Layer:** Instantiate a standard `torch.nn.Linear` layer: `layer = nn.Linear(in_features=K, out_features=M, bias=False)`.
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4. **Load and Transpose Weight:** Load the raw weight tensor `W` and copy its transpose into the layer's parameter: `layer.weight.data.copy_(W.t())`.
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5. **Forward Pass:** Pass the multi-dimensional activation tensor `A` directly to the layer. PyTorch's broadcasting will correctly apply the linear transformation to the last dimension without needing any manual `reshape` operations.
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This procedure creates a PyTorch layer that is a numerically identical replica of the ONNX Runtime's `MatMul` operation, accounting for the subtle but critical difference in weight storage conventions.
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Recommendation: store transposed values in the safetensors file.
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ConvertModel(
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(Conv_684): Conv2d(4, 48, kernel_size=(1, 1), stride=(1, 1))
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(Relu_447): ReLU(inplace=True)
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(Transpose_448): Transpose()
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--------------------------------------------------
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Stage 1 TDF_Block
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--------------------------------------------------
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-- TDF_Block s1
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(Conv_687): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_451): ReLU(inplace=True)
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(Conv_690): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_454): ReLU(inplace=True)
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(Conv_693): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_457): ReLU(inplace=True)
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(MatMul_459): Linear(in_features=3072, out_features=384, bias=False)
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(BatchNormalization_460): BatchNormWrapper(
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(bnu): BatchNormUnsafe(48, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_461): ReLU(inplace=True)
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(MatMul_463): Linear(in_features=384, out_features=3072, bias=False)
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(BatchNormalization_464): BatchNormWrapper(
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(bnu): BatchNormUnsafe(48, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_465): ReLU(inplace=True)
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(Add_466): Add()
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--- Down for s1 (d1)
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(Conv_696): Conv2d(48, 96, kernel_size=(2, 2), stride=(2, 2))
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(Relu_469): ReLU(inplace=True)
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--------------------------------------------------
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Stage 2
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--------------------------------------------------
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-- TDF_Block s2
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(Conv_699): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_472): ReLU(inplace=True)
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(Conv_702): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_475): ReLU(inplace=True)
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(Conv_705): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_478): ReLU(inplace=True)
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(MatMul_480): Linear(in_features=1536, out_features=192, bias=False)
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(BatchNormalization_481): BatchNormWrapper(
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(bnu): BatchNormUnsafe(96, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_482): ReLU(inplace=True)
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(MatMul_484): Linear(in_features=192, out_features=1536, bias=False)
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(BatchNormalization_485): BatchNormWrapper(
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(bnu): BatchNormUnsafe(96, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_486): ReLU(inplace=True)
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(Add_487): Add()
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--- Down for s2 (d2)
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(Conv_708): Conv2d(96, 144, kernel_size=(2, 2), stride=(2, 2))
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(Relu_490): ReLU(inplace=True)
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--------------------------------------------------
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Stage 3
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--------------------------------------------------
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-- TDF_Block s3
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(Conv_711): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_493): ReLU(inplace=True)
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(Conv_714): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_496): ReLU(inplace=True)
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(Conv_717): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_499): ReLU(inplace=True)
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(MatMul_501): Linear(in_features=768, out_features=96, bias=False)
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(BatchNormalization_502): BatchNormWrapper(
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(bnu): BatchNormUnsafe(144, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_503): ReLU(inplace=True)
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(MatMul_505): Linear(in_features=96, out_features=768, bias=False)
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(BatchNormalization_506): BatchNormWrapper(
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(bnu): BatchNormUnsafe(144, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_507): ReLU(inplace=True)
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(Add_508): Add()
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--- Down for s3 (d3)
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(Conv_720): Conv2d(144, 192, kernel_size=(2, 2), stride=(2, 2))
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(Relu_511): ReLU(inplace=True)
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--------------------------------------------------
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Stage 4
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--------------------------------------------------
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-- TDF_Block s4
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(Conv_723): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_514): ReLU(inplace=True)
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(Conv_726): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_517): ReLU(inplace=True)
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(Conv_729): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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(Relu_520): ReLU(inplace=True)
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(MatMul_522): Linear(in_features=384, out_features=48, bias=False)
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(BatchNormalization_523): BatchNormWrapper(
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(bnu): BatchNormUnsafe(192, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_524): ReLU(inplace=True)
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(MatMul_526): Linear(in_features=48, out_features=384, bias=False)
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(BatchNormalization_527): BatchNormWrapper(
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(bnu): BatchNormUnsafe(192, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
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)
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(Relu_528): ReLU(inplace=True)
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(Add_529): Add()
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--- Down for s4 (d4)
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(Conv_732): Conv2d(192, 240, kernel_size=(2, 2), stride=(2, 2))
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(Relu_532): ReLU(inplace=True)
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 5
|
||||
--------------------------------------------------
|
||||
|
||||
-- TDF_Block s5
|
||||
|
||||
(Conv_735): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_535): ReLU(inplace=True)
|
||||
(Conv_738): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_538): ReLU(inplace=True)
|
||||
(Conv_741): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_541): ReLU(inplace=True)
|
||||
(MatMul_543): Linear(in_features=192, out_features=24, bias=False)
|
||||
(BatchNormalization_544): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(240, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_545): ReLU(inplace=True)
|
||||
(MatMul_547): Linear(in_features=24, out_features=192, bias=False)
|
||||
(BatchNormalization_548): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(240, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_549): ReLU(inplace=True)
|
||||
(Add_550): Add()
|
||||
|
||||
--- Down for s5 (d5)
|
||||
|
||||
(Conv_744): Conv2d(240, 288, kernel_size=(2, 2), stride=(2, 2))
|
||||
(Relu_553): ReLU(inplace=True)
|
||||
|
||||
--------------------------------------------------
|
||||
Bottleneck
|
||||
--------------------------------------------------
|
||||
|
||||
(Conv_747): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_556): ReLU(inplace=True)
|
||||
(Conv_750): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_559): ReLU(inplace=True)
|
||||
(Conv_753): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_562): ReLU(inplace=True)
|
||||
(MatMul_564): Linear(in_features=96, out_features=12, bias=False)
|
||||
(BatchNormalization_565): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(288, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_566): ReLU(inplace=True)
|
||||
(MatMul_568): Linear(in_features=12, out_features=96, bias=False)
|
||||
(BatchNormalization_569): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(288, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_570): ReLU(inplace=True)
|
||||
(Add_571): Add()
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 5
|
||||
--------------------------------------------------
|
||||
|
||||
-- Up 5
|
||||
|
||||
(ConvTranspose_572): ConvTranspose2d(288, 240, kernel_size=(2, 2), stride=(2, 2))
|
||||
(BatchNormalization_573): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(240, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_574): ReLU(inplace=True)
|
||||
(Mul_575): mul()
|
||||
|
||||
-- TDF_Block 5
|
||||
|
||||
(Conv_756): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_578): ReLU(inplace=True)
|
||||
(Conv_759): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_581): ReLU(inplace=True)
|
||||
(Conv_762): Conv2d(240, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_584): ReLU(inplace=True)
|
||||
(MatMul_586): Linear(in_features=192, out_features=24, bias=False)
|
||||
(BatchNormalization_587): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(240, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_588): ReLU(inplace=True)
|
||||
(MatMul_590): Linear(in_features=24, out_features=192, bias=False)
|
||||
(BatchNormalization_591): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(240, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_592): ReLU(inplace=True)
|
||||
(Add_593): Add()
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 4
|
||||
--------------------------------------------------
|
||||
|
||||
-- Up 4
|
||||
|
||||
(ConvTranspose_594): ConvTranspose2d(240, 192, kernel_size=(2, 2), stride=(2, 2))
|
||||
(BatchNormalization_595): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(192, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_596): ReLU(inplace=True)
|
||||
(Mul_597): mul()
|
||||
|
||||
-- TDF_Block 4
|
||||
|
||||
(Conv_765): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_600): ReLU(inplace=True)
|
||||
(Conv_768): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_603): ReLU(inplace=True)
|
||||
(Conv_771): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_606): ReLU(inplace=True)
|
||||
(MatMul_608): Linear(in_features=384, out_features=48, bias=False)
|
||||
(BatchNormalization_609): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(192, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_610): ReLU(inplace=True)
|
||||
(MatMul_612): Linear(in_features=48, out_features=384, bias=False)
|
||||
(BatchNormalization_613): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(192, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_614): ReLU(inplace=True)
|
||||
(Add_615): Add()
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 3
|
||||
--------------------------------------------------
|
||||
|
||||
-- Up 3
|
||||
|
||||
(ConvTranspose_616): ConvTranspose2d(192, 144, kernel_size=(2, 2), stride=(2, 2))
|
||||
(BatchNormalization_617): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(144, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_618): ReLU(inplace=True)
|
||||
(Mul_619): mul()
|
||||
|
||||
-- TDF_Block 3
|
||||
|
||||
(Conv_774): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_622): ReLU(inplace=True)
|
||||
(Conv_777): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_625): ReLU(inplace=True)
|
||||
(Conv_780): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_628): ReLU(inplace=True)
|
||||
(MatMul_630): Linear(in_features=768, out_features=96, bias=False)
|
||||
(BatchNormalization_631): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(144, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_632): ReLU(inplace=True)
|
||||
(MatMul_634): Linear(in_features=96, out_features=768, bias=False)
|
||||
(BatchNormalization_635): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(144, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_636): ReLU(inplace=True)
|
||||
(Add_637): Add()
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 2
|
||||
--------------------------------------------------
|
||||
|
||||
-- Up 2
|
||||
|
||||
(ConvTranspose_638): ConvTranspose2d(144, 96, kernel_size=(2, 2), stride=(2, 2))
|
||||
(BatchNormalization_639): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(96, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_640): ReLU(inplace=True)
|
||||
(Mul_641): mul()
|
||||
|
||||
-- TDF_Block 2
|
||||
|
||||
(Conv_783): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_644): ReLU(inplace=True)
|
||||
(Conv_786): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_647): ReLU(inplace=True)
|
||||
(Conv_789): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_650): ReLU(inplace=True)
|
||||
(MatMul_652): Linear(in_features=1536, out_features=192, bias=False)
|
||||
(BatchNormalization_653): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(96, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_654): ReLU(inplace=True)
|
||||
(MatMul_656): Linear(in_features=192, out_features=1536, bias=False)
|
||||
(BatchNormalization_657): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(96, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_658): ReLU(inplace=True)
|
||||
(Add_659): Add()
|
||||
|
||||
--------------------------------------------------
|
||||
Stage 1
|
||||
--------------------------------------------------
|
||||
|
||||
-- Up 1
|
||||
|
||||
(ConvTranspose_660): ConvTranspose2d(96, 48, kernel_size=(2, 2), stride=(2, 2))
|
||||
(BatchNormalization_661): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(48, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_662): ReLU(inplace=True)
|
||||
(Mul_663): mul()
|
||||
(Conv_792): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_666): ReLU(inplace=True)
|
||||
(Conv_795): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_669): ReLU(inplace=True)
|
||||
(Conv_798): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(Relu_672): ReLU(inplace=True)
|
||||
(MatMul_674): Linear(in_features=3072, out_features=384, bias=False)
|
||||
(BatchNormalization_675): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(48, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_676): ReLU(inplace=True)
|
||||
(MatMul_678): Linear(in_features=384, out_features=3072, bias=False)
|
||||
(BatchNormalization_679): BatchNormWrapper(
|
||||
(bnu): BatchNormUnsafe(48, eps=9.999999747378752e-06, momentum=0.8999999761581421, affine=True, track_running_stats=True)
|
||||
)
|
||||
(Relu_680): ReLU(inplace=True)
|
||||
(Add_681): Add()
|
||||
|
||||
-- TDF_Block 2
|
||||
|
||||
--------------------------------------------------
|
||||
Final
|
||||
--------------------------------------------------
|
||||
|
||||
(Transpose_682): Transpose()
|
||||
(Conv_output): Conv2d(48, 4, kernel_size=(1, 1), stride=(1, 1))
|
||||
)
|
||||
@@ -0,0 +1,201 @@
|
||||
Importing module 'MDX_Net' from 'modules'...
|
||||
Successfully imported class 'MDX_Net'.
|
||||
--- PyTorch Model Structure ---
|
||||
model.MDX_Net: MDX_Net
|
||||
model.initial_conv: Conv2d
|
||||
model.initial_relu: ReLU
|
||||
model.initial_transpose: Transpose
|
||||
model.enc_stages.ModuleList: ModuleList
|
||||
model.enc_stages.0.TDF_Block: TDF_Block
|
||||
model.enc_stages.0.time_branch.TimeBranch: TimeBranch
|
||||
model.enc_stages.0.time_branch.sequence.0: Conv2d
|
||||
model.enc_stages.0.time_branch.sequence.1: ReLU
|
||||
model.enc_stages.0.time_branch.sequence.2: Conv2d
|
||||
model.enc_stages.0.time_branch.sequence.3: ReLU
|
||||
model.enc_stages.0.time_branch.sequence.4: Conv2d
|
||||
model.enc_stages.0.time_branch.sequence.5: ReLU
|
||||
model.enc_stages.0.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.enc_stages.0.freq_branch.sequence.0: Linear
|
||||
model.enc_stages.0.freq_branch.sequence.1: BatchNorm2d
|
||||
model.enc_stages.0.freq_branch.sequence.2: ReLU
|
||||
model.enc_stages.0.freq_branch.sequence.3: Linear
|
||||
model.enc_stages.0.freq_branch.sequence.4: BatchNorm2d
|
||||
model.enc_stages.0.freq_branch.sequence.5: ReLU
|
||||
model.enc_stages.1.0: Conv2d
|
||||
model.enc_stages.1.1: ReLU
|
||||
model.enc_stages.2.TDF_Block: TDF_Block
|
||||
model.enc_stages.2.time_branch.TimeBranch: TimeBranch
|
||||
model.enc_stages.2.time_branch.sequence.0: Conv2d
|
||||
model.enc_stages.2.time_branch.sequence.1: ReLU
|
||||
model.enc_stages.2.time_branch.sequence.2: Conv2d
|
||||
model.enc_stages.2.time_branch.sequence.3: ReLU
|
||||
model.enc_stages.2.time_branch.sequence.4: Conv2d
|
||||
model.enc_stages.2.time_branch.sequence.5: ReLU
|
||||
model.enc_stages.2.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.enc_stages.2.freq_branch.sequence.0: Linear
|
||||
model.enc_stages.2.freq_branch.sequence.1: BatchNorm2d
|
||||
model.enc_stages.2.freq_branch.sequence.2: ReLU
|
||||
model.enc_stages.2.freq_branch.sequence.3: Linear
|
||||
model.enc_stages.2.freq_branch.sequence.4: BatchNorm2d
|
||||
model.enc_stages.2.freq_branch.sequence.5: ReLU
|
||||
model.enc_stages.3.0: Conv2d
|
||||
model.enc_stages.3.1: ReLU
|
||||
model.enc_stages.4.TDF_Block: TDF_Block
|
||||
model.enc_stages.4.time_branch.TimeBranch: TimeBranch
|
||||
model.enc_stages.4.time_branch.sequence.0: Conv2d
|
||||
model.enc_stages.4.time_branch.sequence.1: ReLU
|
||||
model.enc_stages.4.time_branch.sequence.2: Conv2d
|
||||
model.enc_stages.4.time_branch.sequence.3: ReLU
|
||||
model.enc_stages.4.time_branch.sequence.4: Conv2d
|
||||
model.enc_stages.4.time_branch.sequence.5: ReLU
|
||||
model.enc_stages.4.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.enc_stages.4.freq_branch.sequence.0: Linear
|
||||
model.enc_stages.4.freq_branch.sequence.1: BatchNorm2d
|
||||
model.enc_stages.4.freq_branch.sequence.2: ReLU
|
||||
model.enc_stages.4.freq_branch.sequence.3: Linear
|
||||
model.enc_stages.4.freq_branch.sequence.4: BatchNorm2d
|
||||
model.enc_stages.4.freq_branch.sequence.5: ReLU
|
||||
model.enc_stages.5.0: Conv2d
|
||||
model.enc_stages.5.1: ReLU
|
||||
model.enc_stages.6.TDF_Block: TDF_Block
|
||||
model.enc_stages.6.time_branch.TimeBranch: TimeBranch
|
||||
model.enc_stages.6.time_branch.sequence.0: Conv2d
|
||||
model.enc_stages.6.time_branch.sequence.1: ReLU
|
||||
model.enc_stages.6.time_branch.sequence.2: Conv2d
|
||||
model.enc_stages.6.time_branch.sequence.3: ReLU
|
||||
model.enc_stages.6.time_branch.sequence.4: Conv2d
|
||||
model.enc_stages.6.time_branch.sequence.5: ReLU
|
||||
model.enc_stages.6.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.enc_stages.6.freq_branch.sequence.0: Linear
|
||||
model.enc_stages.6.freq_branch.sequence.1: BatchNorm2d
|
||||
model.enc_stages.6.freq_branch.sequence.2: ReLU
|
||||
model.enc_stages.6.freq_branch.sequence.3: Linear
|
||||
model.enc_stages.6.freq_branch.sequence.4: BatchNorm2d
|
||||
model.enc_stages.6.freq_branch.sequence.5: ReLU
|
||||
model.enc_stages.7.0: Conv2d
|
||||
model.enc_stages.7.1: ReLU
|
||||
model.enc_stages.8.TDF_Block: TDF_Block
|
||||
model.enc_stages.8.time_branch.TimeBranch: TimeBranch
|
||||
model.enc_stages.8.time_branch.sequence.0: Conv2d
|
||||
model.enc_stages.8.time_branch.sequence.1: ReLU
|
||||
model.enc_stages.8.time_branch.sequence.2: Conv2d
|
||||
model.enc_stages.8.time_branch.sequence.3: ReLU
|
||||
model.enc_stages.8.time_branch.sequence.4: Conv2d
|
||||
model.enc_stages.8.time_branch.sequence.5: ReLU
|
||||
model.enc_stages.8.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.enc_stages.8.freq_branch.sequence.0: Linear
|
||||
model.enc_stages.8.freq_branch.sequence.1: BatchNorm2d
|
||||
model.enc_stages.8.freq_branch.sequence.2: ReLU
|
||||
model.enc_stages.8.freq_branch.sequence.3: Linear
|
||||
model.enc_stages.8.freq_branch.sequence.4: BatchNorm2d
|
||||
model.enc_stages.8.freq_branch.sequence.5: ReLU
|
||||
model.enc_stages.9.0: Conv2d
|
||||
model.enc_stages.9.1: ReLU
|
||||
model.bottleneck.TDF_Block: TDF_Block
|
||||
model.bottleneck.time_branch.TimeBranch: TimeBranch
|
||||
model.bottleneck.time_branch.sequence.0: Conv2d
|
||||
model.bottleneck.time_branch.sequence.1: ReLU
|
||||
model.bottleneck.time_branch.sequence.2: Conv2d
|
||||
model.bottleneck.time_branch.sequence.3: ReLU
|
||||
model.bottleneck.time_branch.sequence.4: Conv2d
|
||||
model.bottleneck.time_branch.sequence.5: ReLU
|
||||
model.bottleneck.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.bottleneck.freq_branch.sequence.0: Linear
|
||||
model.bottleneck.freq_branch.sequence.1: BatchNorm2d
|
||||
model.bottleneck.freq_branch.sequence.2: ReLU
|
||||
model.bottleneck.freq_branch.sequence.3: Linear
|
||||
model.bottleneck.freq_branch.sequence.4: BatchNorm2d
|
||||
model.bottleneck.freq_branch.sequence.5: ReLU
|
||||
model.dec_stages.ModuleList: ModuleList
|
||||
model.dec_stages.0.0: ConvTranspose2d
|
||||
model.dec_stages.0.1: BatchNorm2d
|
||||
model.dec_stages.0.2: ReLU
|
||||
model.dec_stages.1.TDF_Block: TDF_Block
|
||||
model.dec_stages.1.time_branch.TimeBranch: TimeBranch
|
||||
model.dec_stages.1.time_branch.sequence.0: Conv2d
|
||||
model.dec_stages.1.time_branch.sequence.1: ReLU
|
||||
model.dec_stages.1.time_branch.sequence.2: Conv2d
|
||||
model.dec_stages.1.time_branch.sequence.3: ReLU
|
||||
model.dec_stages.1.time_branch.sequence.4: Conv2d
|
||||
model.dec_stages.1.time_branch.sequence.5: ReLU
|
||||
model.dec_stages.1.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.dec_stages.1.freq_branch.sequence.0: Linear
|
||||
model.dec_stages.1.freq_branch.sequence.1: BatchNorm2d
|
||||
model.dec_stages.1.freq_branch.sequence.2: ReLU
|
||||
model.dec_stages.1.freq_branch.sequence.3: Linear
|
||||
model.dec_stages.1.freq_branch.sequence.4: BatchNorm2d
|
||||
model.dec_stages.1.freq_branch.sequence.5: ReLU
|
||||
model.dec_stages.2.0: ConvTranspose2d
|
||||
model.dec_stages.2.1: BatchNorm2d
|
||||
model.dec_stages.2.2: ReLU
|
||||
model.dec_stages.3.TDF_Block: TDF_Block
|
||||
model.dec_stages.3.time_branch.TimeBranch: TimeBranch
|
||||
model.dec_stages.3.time_branch.sequence.0: Conv2d
|
||||
model.dec_stages.3.time_branch.sequence.1: ReLU
|
||||
model.dec_stages.3.time_branch.sequence.2: Conv2d
|
||||
model.dec_stages.3.time_branch.sequence.3: ReLU
|
||||
model.dec_stages.3.time_branch.sequence.4: Conv2d
|
||||
model.dec_stages.3.time_branch.sequence.5: ReLU
|
||||
model.dec_stages.3.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.dec_stages.3.freq_branch.sequence.0: Linear
|
||||
model.dec_stages.3.freq_branch.sequence.1: BatchNorm2d
|
||||
model.dec_stages.3.freq_branch.sequence.2: ReLU
|
||||
model.dec_stages.3.freq_branch.sequence.3: Linear
|
||||
model.dec_stages.3.freq_branch.sequence.4: BatchNorm2d
|
||||
model.dec_stages.3.freq_branch.sequence.5: ReLU
|
||||
model.dec_stages.4.0: ConvTranspose2d
|
||||
model.dec_stages.4.1: BatchNorm2d
|
||||
model.dec_stages.4.2: ReLU
|
||||
model.dec_stages.5.TDF_Block: TDF_Block
|
||||
model.dec_stages.5.time_branch.TimeBranch: TimeBranch
|
||||
model.dec_stages.5.time_branch.sequence.0: Conv2d
|
||||
model.dec_stages.5.time_branch.sequence.1: ReLU
|
||||
model.dec_stages.5.time_branch.sequence.2: Conv2d
|
||||
model.dec_stages.5.time_branch.sequence.3: ReLU
|
||||
model.dec_stages.5.time_branch.sequence.4: Conv2d
|
||||
model.dec_stages.5.time_branch.sequence.5: ReLU
|
||||
model.dec_stages.5.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.dec_stages.5.freq_branch.sequence.0: Linear
|
||||
model.dec_stages.5.freq_branch.sequence.1: BatchNorm2d
|
||||
model.dec_stages.5.freq_branch.sequence.2: ReLU
|
||||
model.dec_stages.5.freq_branch.sequence.3: Linear
|
||||
model.dec_stages.5.freq_branch.sequence.4: BatchNorm2d
|
||||
model.dec_stages.5.freq_branch.sequence.5: ReLU
|
||||
model.dec_stages.6.0: ConvTranspose2d
|
||||
model.dec_stages.6.1: BatchNorm2d
|
||||
model.dec_stages.6.2: ReLU
|
||||
model.dec_stages.7.TDF_Block: TDF_Block
|
||||
model.dec_stages.7.time_branch.TimeBranch: TimeBranch
|
||||
model.dec_stages.7.time_branch.sequence.0: Conv2d
|
||||
model.dec_stages.7.time_branch.sequence.1: ReLU
|
||||
model.dec_stages.7.time_branch.sequence.2: Conv2d
|
||||
model.dec_stages.7.time_branch.sequence.3: ReLU
|
||||
model.dec_stages.7.time_branch.sequence.4: Conv2d
|
||||
model.dec_stages.7.time_branch.sequence.5: ReLU
|
||||
model.dec_stages.7.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.dec_stages.7.freq_branch.sequence.0: Linear
|
||||
model.dec_stages.7.freq_branch.sequence.1: BatchNorm2d
|
||||
model.dec_stages.7.freq_branch.sequence.2: ReLU
|
||||
model.dec_stages.7.freq_branch.sequence.3: Linear
|
||||
model.dec_stages.7.freq_branch.sequence.4: BatchNorm2d
|
||||
model.dec_stages.7.freq_branch.sequence.5: ReLU
|
||||
model.dec_stages.8.0: ConvTranspose2d
|
||||
model.dec_stages.8.1: BatchNorm2d
|
||||
model.dec_stages.8.2: ReLU
|
||||
model.dec_stages.9.TDF_Block: TDF_Block
|
||||
model.dec_stages.9.time_branch.TimeBranch: TimeBranch
|
||||
model.dec_stages.9.time_branch.sequence.0: Conv2d
|
||||
model.dec_stages.9.time_branch.sequence.1: ReLU
|
||||
model.dec_stages.9.time_branch.sequence.2: Conv2d
|
||||
model.dec_stages.9.time_branch.sequence.3: ReLU
|
||||
model.dec_stages.9.time_branch.sequence.4: Conv2d
|
||||
model.dec_stages.9.time_branch.sequence.5: ReLU
|
||||
model.dec_stages.9.freq_branch.FrequencyBranch: FrequencyBranch
|
||||
model.dec_stages.9.freq_branch.sequence.0: Linear
|
||||
model.dec_stages.9.freq_branch.sequence.1: BatchNorm2d
|
||||
model.dec_stages.9.freq_branch.sequence.2: ReLU
|
||||
model.dec_stages.9.freq_branch.sequence.3: Linear
|
||||
model.dec_stages.9.freq_branch.sequence.4: BatchNorm2d
|
||||
model.dec_stages.9.freq_branch.sequence.5: ReLU
|
||||
model.final_transpose: Transpose
|
||||
model.final_conv: Conv2d
|
||||
@@ -0,0 +1,642 @@
|
||||
[000] Conv_0 type: Conv W inputs: ['input', '685', '686'] outputs: ['684']
|
||||
- [0] from This is the input of the net
|
||||
- [1] from weight of shape torch.Size([48, 4, 1, 1]) [name: 685]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 686]
|
||||
[001] Relu_1 type: Relu inputs: ['684'] outputs: ['447']
|
||||
- [0] from output of Conv_0 [name: 684]
|
||||
[002] Transpose_2 type: Transpose inputs: ['447'] outputs: ['448']
|
||||
- [0] from output of Relu_1 [name: 447]
|
||||
|
||||
-------------------------------------------
|
||||
Stage 1
|
||||
-------------------------------------------
|
||||
|
||||
[003] Conv_3 type: Conv W inputs: ['448', '688', '689'] outputs: ['687']
|
||||
- [0] from output of Transpose_2 [name: 448]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 688]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 689]
|
||||
[004] Relu_4 type: Relu inputs: ['687'] outputs: ['451']
|
||||
- [0] from output of Conv_3 [name: 687]
|
||||
[005] Conv_5 type: Conv W inputs: ['451', '691', '692'] outputs: ['690']
|
||||
- [0] from output of Relu_4 [name: 451]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 691]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 692]
|
||||
[006] Relu_6 type: Relu inputs: ['690'] outputs: ['454']
|
||||
- [0] from output of Conv_5 [name: 690]
|
||||
[007] Conv_7 type: Conv W inputs: ['454', '694', '695'] outputs: ['693']
|
||||
- [0] from output of Relu_6 [name: 454]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 694]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 695]
|
||||
[008] Relu_8 type: Relu inputs: ['693'] outputs: ['457']
|
||||
- [0] from output of Conv_7 [name: 693]
|
||||
|
||||
[009] MatMul_9 type: MatMul W inputs: ['457', '801'] outputs: ['459']
|
||||
- [0] from output of Relu_8 [name: 457]
|
||||
- [1] from weight of shape torch.Size([3072, 384]) [name: 801]
|
||||
[010] BatchNormalization_10 type: BatchNormalization W inputs: ['459', 'encoding_blocks.0.tdf.1.weight', 'encoding_blocks.0.tdf.1.bias', 'encoding_blocks.0.tdf.1.running_mean', 'encoding_blocks.0.tdf.1.running_var'] outputs: ['460']
|
||||
- [0] from output of MatMul_9 [name: 459]
|
||||
- [1] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.1.running_var]
|
||||
[011] Relu_11 type: Relu inputs: ['460'] outputs: ['461']
|
||||
- [0] from output of BatchNormalization_10 [name: 460]
|
||||
[012] MatMul_12 type: MatMul W inputs: ['461', '802'] outputs: ['463']
|
||||
- [0] from output of Relu_11 [name: 461]
|
||||
- [1] from weight of shape torch.Size([384, 3072]) [name: 802]
|
||||
[013] BatchNormalization_13 type: BatchNormalization W inputs: ['463', 'encoding_blocks.0.tdf.4.weight', 'encoding_blocks.0.tdf.4.bias', 'encoding_blocks.0.tdf.4.running_mean', 'encoding_blocks.0.tdf.4.running_var'] outputs: ['464']
|
||||
- [0] from output of MatMul_12 [name: 463]
|
||||
- [1] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([48]) [name: encoding_blocks.0.tdf.4.running_var]
|
||||
[014] Relu_14 type: Relu inputs: ['464'] outputs: ['465']
|
||||
- [0] from output of BatchNormalization_13 [name: 464]
|
||||
|
||||
[015] Add_15 type: Add inputs: ['457', '465'] outputs: ['466']
|
||||
- [0] from output of Relu_8 [name: 457]
|
||||
- [1] from output of Relu_14 [name: 465]
|
||||
|
||||
DOWN 1
|
||||
|
||||
[016] Conv_16 type: Conv W inputs: ['466', '697', '698'] outputs: ['696']
|
||||
- [0] from output of Add_15 [name: 466]
|
||||
- [1] from weight of shape torch.Size([96, 48, 2, 2]) [name: 697]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 698]
|
||||
[017] Relu_17 type: Relu inputs: ['696'] outputs: ['469']
|
||||
- [0] from output of Conv_16 [name: 696]
|
||||
|
||||
-------------------------------------------
|
||||
Stage 2
|
||||
-------------------------------------------
|
||||
|
||||
[018] Conv_18 type: Conv W inputs: ['469', '700', '701'] outputs: ['699']
|
||||
- [0] from output of Relu_17 [name: 469]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 700]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 701]
|
||||
[019] Relu_19 type: Relu inputs: ['699'] outputs: ['472']
|
||||
- [0] from output of Conv_18 [name: 699]
|
||||
[020] Conv_20 type: Conv W inputs: ['472', '703', '704'] outputs: ['702']
|
||||
- [0] from output of Relu_19 [name: 472]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 703]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 704]
|
||||
[021] Relu_21 type: Relu inputs: ['702'] outputs: ['475']
|
||||
- [0] from output of Conv_20 [name: 702]
|
||||
[022] Conv_22 type: Conv W inputs: ['475', '706', '707'] outputs: ['705']
|
||||
- [0] from output of Relu_21 [name: 475]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 706]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 707]
|
||||
[023] Relu_23 type: Relu inputs: ['705'] outputs: ['478']
|
||||
- [0] from output of Conv_22 [name: 705]
|
||||
|
||||
[024] MatMul_24 type: MatMul W inputs: ['478', '803'] outputs: ['480']
|
||||
- [0] from output of Relu_23 [name: 478]
|
||||
- [1] from weight of shape torch.Size([1536, 192]) [name: 803]
|
||||
[025] BatchNormalization_25 type: BatchNormalization W inputs: ['480', 'encoding_blocks.1.tdf.1.weight', 'encoding_blocks.1.tdf.1.bias', 'encoding_blocks.1.tdf.1.running_mean', 'encoding_blocks.1.tdf.1.running_var'] outputs: ['481']
|
||||
- [0] from output of MatMul_24 [name: 480]
|
||||
- [1] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.1.running_var]
|
||||
[026] Relu_26 type: Relu inputs: ['481'] outputs: ['482']
|
||||
- [0] from output of BatchNormalization_25 [name: 481]
|
||||
[027] MatMul_27 type: MatMul W inputs: ['482', '804'] outputs: ['484']
|
||||
- [0] from output of Relu_26 [name: 482]
|
||||
- [1] from weight of shape torch.Size([192, 1536]) [name: 804]
|
||||
[028] BatchNormalization_28 type: BatchNormalization W inputs: ['484', 'encoding_blocks.1.tdf.4.weight', 'encoding_blocks.1.tdf.4.bias', 'encoding_blocks.1.tdf.4.running_mean', 'encoding_blocks.1.tdf.4.running_var'] outputs: ['485']
|
||||
- [0] from output of MatMul_27 [name: 484]
|
||||
- [1] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([96]) [name: encoding_blocks.1.tdf.4.running_var]
|
||||
[029] Relu_29 type: Relu inputs: ['485'] outputs: ['486']
|
||||
- [0] from output of BatchNormalization_28 [name: 485]
|
||||
|
||||
[030] Add_30 type: Add inputs: ['478', '486'] outputs: ['487']
|
||||
- [0] from output of Relu_23 [name: 478]
|
||||
- [1] from output of Relu_29 [name: 486]
|
||||
|
||||
DOWN 2
|
||||
|
||||
[031] Conv_31 type: Conv W inputs: ['487', '709', '710'] outputs: ['708']
|
||||
- [0] from output of Add_30 [name: 487]
|
||||
- [1] from weight of shape torch.Size([144, 96, 2, 2]) [name: 709]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 710]
|
||||
[032] Relu_32 type: Relu inputs: ['708'] outputs: ['490']
|
||||
- [0] from output of Conv_31 [name: 708]
|
||||
|
||||
-------------------------------------------
|
||||
Stage 3
|
||||
-------------------------------------------
|
||||
|
||||
[033] Conv_33 type: Conv W inputs: ['490', '712', '713'] outputs: ['711']
|
||||
- [0] from output of Relu_32 [name: 490]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 712]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 713]
|
||||
[034] Relu_34 type: Relu inputs: ['711'] outputs: ['493']
|
||||
- [0] from output of Conv_33 [name: 711]
|
||||
[035] Conv_35 type: Conv W inputs: ['493', '715', '716'] outputs: ['714']
|
||||
- [0] from output of Relu_34 [name: 493]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 715]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 716]
|
||||
[036] Relu_36 type: Relu inputs: ['714'] outputs: ['496']
|
||||
- [0] from output of Conv_35 [name: 714]
|
||||
[037] Conv_37 type: Conv W inputs: ['496', '718', '719'] outputs: ['717']
|
||||
- [0] from output of Relu_36 [name: 496]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 718]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 719]
|
||||
[038] Relu_38 type: Relu inputs: ['717'] outputs: ['499']
|
||||
- [0] from output of Conv_37 [name: 717]
|
||||
[039] MatMul_39 type: MatMul W inputs: ['499', '805'] outputs: ['501']
|
||||
- [0] from output of Relu_38 [name: 499]
|
||||
- [1] from weight of shape torch.Size([768, 96]) [name: 805]
|
||||
[040] BatchNormalization_40 type: BatchNormalization W inputs: ['501', 'encoding_blocks.2.tdf.1.weight', 'encoding_blocks.2.tdf.1.bias', 'encoding_blocks.2.tdf.1.running_mean', 'encoding_blocks.2.tdf.1.running_var'] outputs: ['502']
|
||||
- [0] from output of MatMul_39 [name: 501]
|
||||
- [1] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.1.running_var]
|
||||
[041] Relu_41 type: Relu inputs: ['502'] outputs: ['503']
|
||||
- [0] from output of BatchNormalization_40 [name: 502]
|
||||
[042] MatMul_42 type: MatMul W inputs: ['503', '806'] outputs: ['505']
|
||||
- [0] from output of Relu_41 [name: 503]
|
||||
- [1] from weight of shape torch.Size([96, 768]) [name: 806]
|
||||
[043] BatchNormalization_43 type: BatchNormalization W inputs: ['505', 'encoding_blocks.2.tdf.4.weight', 'encoding_blocks.2.tdf.4.bias', 'encoding_blocks.2.tdf.4.running_mean', 'encoding_blocks.2.tdf.4.running_var'] outputs: ['506']
|
||||
- [0] from output of MatMul_42 [name: 505]
|
||||
- [1] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([144]) [name: encoding_blocks.2.tdf.4.running_var]
|
||||
[044] Relu_44 type: Relu inputs: ['506'] outputs: ['507']
|
||||
- [0] from output of BatchNormalization_43 [name: 506]
|
||||
[045] Add_45 type: Add inputs: ['499', '507'] outputs: ['508']
|
||||
- [0] from output of Relu_38 [name: 499]
|
||||
- [1] from output of Relu_44 [name: 507]
|
||||
|
||||
DOWN 3
|
||||
|
||||
[046] Conv_46 type: Conv W inputs: ['508', '721', '722'] outputs: ['720']
|
||||
- [0] from output of Add_45 [name: 508]
|
||||
- [1] from weight of shape torch.Size([192, 144, 2, 2]) [name: 721]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 722]
|
||||
[047] Relu_47 type: Relu inputs: ['720'] outputs: ['511']
|
||||
- [0] from output of Conv_46 [name: 720]
|
||||
|
||||
-------------------------------------------
|
||||
Stage 4
|
||||
-------------------------------------------
|
||||
|
||||
[048] Conv_48 type: Conv W inputs: ['511', '724', '725'] outputs: ['723']
|
||||
- [0] from output of Relu_47 [name: 511]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 724]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 725]
|
||||
[049] Relu_49 type: Relu inputs: ['723'] outputs: ['514']
|
||||
- [0] from output of Conv_48 [name: 723]
|
||||
[050] Conv_50 type: Conv W inputs: ['514', '727', '728'] outputs: ['726']
|
||||
- [0] from output of Relu_49 [name: 514]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 727]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 728]
|
||||
[051] Relu_51 type: Relu inputs: ['726'] outputs: ['517']
|
||||
- [0] from output of Conv_50 [name: 726]
|
||||
[052] Conv_52 type: Conv W inputs: ['517', '730', '731'] outputs: ['729']
|
||||
- [0] from output of Relu_51 [name: 517]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 730]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 731]
|
||||
[053] Relu_53 type: Relu inputs: ['729'] outputs: ['520']
|
||||
- [0] from output of Conv_52 [name: 729]
|
||||
[054] MatMul_54 type: MatMul W inputs: ['520', '807'] outputs: ['522']
|
||||
- [0] from output of Relu_53 [name: 520]
|
||||
- [1] from weight of shape torch.Size([384, 48]) [name: 807]
|
||||
[055] BatchNormalization_55 type: BatchNormalization W inputs: ['522', 'encoding_blocks.3.tdf.1.weight', 'encoding_blocks.3.tdf.1.bias', 'encoding_blocks.3.tdf.1.running_mean', 'encoding_blocks.3.tdf.1.running_var'] outputs: ['523']
|
||||
- [0] from output of MatMul_54 [name: 522]
|
||||
- [1] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.1.running_var]
|
||||
[056] Relu_56 type: Relu inputs: ['523'] outputs: ['524']
|
||||
- [0] from output of BatchNormalization_55 [name: 523]
|
||||
[057] MatMul_57 type: MatMul W inputs: ['524', '808'] outputs: ['526']
|
||||
- [0] from output of Relu_56 [name: 524]
|
||||
- [1] from weight of shape torch.Size([48, 384]) [name: 808]
|
||||
[058] BatchNormalization_58 type: BatchNormalization W inputs: ['526', 'encoding_blocks.3.tdf.4.weight', 'encoding_blocks.3.tdf.4.bias', 'encoding_blocks.3.tdf.4.running_mean', 'encoding_blocks.3.tdf.4.running_var'] outputs: ['527']
|
||||
- [0] from output of MatMul_57 [name: 526]
|
||||
- [1] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([192]) [name: encoding_blocks.3.tdf.4.running_var]
|
||||
[059] Relu_59 type: Relu inputs: ['527'] outputs: ['528']
|
||||
- [0] from output of BatchNormalization_58 [name: 527]
|
||||
[060] Add_60 type: Add inputs: ['520', '528'] outputs: ['529']
|
||||
- [0] from output of Relu_53 [name: 520]
|
||||
- [1] from output of Relu_59 [name: 528]
|
||||
|
||||
DOWN 4
|
||||
|
||||
[061] Conv_61 type: Conv W inputs: ['529', '733', '734'] outputs: ['732']
|
||||
- [0] from output of Add_60 [name: 529]
|
||||
- [1] from weight of shape torch.Size([240, 192, 2, 2]) [name: 733]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 734]
|
||||
[062] Relu_62 type: Relu inputs: ['732'] outputs: ['532']
|
||||
- [0] from output of Conv_61 [name: 732]
|
||||
|
||||
-------------------------------------------
|
||||
Stage 5
|
||||
-------------------------------------------
|
||||
|
||||
[063] Conv_63 type: Conv W inputs: ['532', '736', '737'] outputs: ['735']
|
||||
- [0] from output of Relu_62 [name: 532]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 736]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 737]
|
||||
[064] Relu_64 type: Relu inputs: ['735'] outputs: ['535']
|
||||
- [0] from output of Conv_63 [name: 735]
|
||||
[065] Conv_65 type: Conv W inputs: ['535', '739', '740'] outputs: ['738']
|
||||
- [0] from output of Relu_64 [name: 535]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 739]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 740]
|
||||
[066] Relu_66 type: Relu inputs: ['738'] outputs: ['538']
|
||||
- [0] from output of Conv_65 [name: 738]
|
||||
[067] Conv_67 type: Conv W inputs: ['538', '742', '743'] outputs: ['741']
|
||||
- [0] from output of Relu_66 [name: 538]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 742]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 743]
|
||||
[068] Relu_68 type: Relu inputs: ['741'] outputs: ['541']
|
||||
- [0] from output of Conv_67 [name: 741]
|
||||
[069] MatMul_69 type: MatMul W inputs: ['541', '809'] outputs: ['543']
|
||||
- [0] from output of Relu_68 [name: 541]
|
||||
- [1] from weight of shape torch.Size([192, 24]) [name: 809]
|
||||
[070] BatchNormalization_70 type: BatchNormalization W inputs: ['543', 'encoding_blocks.4.tdf.1.weight', 'encoding_blocks.4.tdf.1.bias', 'encoding_blocks.4.tdf.1.running_mean', 'encoding_blocks.4.tdf.1.running_var'] outputs: ['544']
|
||||
- [0] from output of MatMul_69 [name: 543]
|
||||
- [1] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.1.running_var]
|
||||
[071] Relu_71 type: Relu inputs: ['544'] outputs: ['545']
|
||||
- [0] from output of BatchNormalization_70 [name: 544]
|
||||
[072] MatMul_72 type: MatMul W inputs: ['545', '810'] outputs: ['547']
|
||||
- [0] from output of Relu_71 [name: 545]
|
||||
- [1] from weight of shape torch.Size([24, 192]) [name: 810]
|
||||
[073] BatchNormalization_73 type: BatchNormalization W inputs: ['547', 'encoding_blocks.4.tdf.4.weight', 'encoding_blocks.4.tdf.4.bias', 'encoding_blocks.4.tdf.4.running_mean', 'encoding_blocks.4.tdf.4.running_var'] outputs: ['548']
|
||||
- [0] from output of MatMul_72 [name: 547]
|
||||
- [1] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([240]) [name: encoding_blocks.4.tdf.4.running_var]
|
||||
[074] Relu_74 type: Relu inputs: ['548'] outputs: ['549']
|
||||
- [0] from output of BatchNormalization_73 [name: 548]
|
||||
[075] Add_75 type: Add inputs: ['541', '549'] outputs: ['550']
|
||||
- [0] from output of Relu_68 [name: 541]
|
||||
- [1] from output of Relu_74 [name: 549]
|
||||
|
||||
DOWN 5
|
||||
|
||||
[076] Conv_76 type: Conv W inputs: ['550', '745', '746'] outputs: ['744']
|
||||
- [0] from output of Add_75 [name: 550]
|
||||
- [1] from weight of shape torch.Size([288, 240, 2, 2]) [name: 745]
|
||||
- [2] from weight of shape torch.Size([288]) [name: 746]
|
||||
[077] Relu_77 type: Relu inputs: ['744'] outputs: ['553']
|
||||
- [0] from output of Conv_76 [name: 744]
|
||||
|
||||
-------------------------------------------
|
||||
Bottleneck
|
||||
-------------------------------------------
|
||||
|
||||
[078] Conv_78 type: Conv W inputs: ['553', '748', '749'] outputs: ['747']
|
||||
- [0] from output of Relu_77 [name: 553]
|
||||
- [1] from weight of shape torch.Size([288, 288, 3, 3]) [name: 748]
|
||||
- [2] from weight of shape torch.Size([288]) [name: 749]
|
||||
[079] Relu_79 type: Relu inputs: ['747'] outputs: ['556']
|
||||
- [0] from output of Conv_78 [name: 747]
|
||||
[080] Conv_80 type: Conv W inputs: ['556', '751', '752'] outputs: ['750']
|
||||
- [0] from output of Relu_79 [name: 556]
|
||||
- [1] from weight of shape torch.Size([288, 288, 3, 3]) [name: 751]
|
||||
- [2] from weight of shape torch.Size([288]) [name: 752]
|
||||
[081] Relu_81 type: Relu inputs: ['750'] outputs: ['559']
|
||||
- [0] from output of Conv_80 [name: 750]
|
||||
[082] Conv_82 type: Conv W inputs: ['559', '754', '755'] outputs: ['753']
|
||||
- [0] from output of Relu_81 [name: 559]
|
||||
- [1] from weight of shape torch.Size([288, 288, 3, 3]) [name: 754]
|
||||
- [2] from weight of shape torch.Size([288]) [name: 755]
|
||||
[083] Relu_83 type: Relu inputs: ['753'] outputs: ['562']
|
||||
- [0] from output of Conv_82 [name: 753]
|
||||
[084] MatMul_84 type: MatMul W inputs: ['562', '811'] outputs: ['564']
|
||||
- [0] from output of Relu_83 [name: 562]
|
||||
- [1] from weight of shape torch.Size([96, 12]) [name: 811]
|
||||
[085] BatchNormalization_85 type: BatchNormalization W inputs: ['564', 'bottleneck_block.tdf.1.weight', 'bottleneck_block.tdf.1.bias', 'bottleneck_block.tdf.1.running_mean', 'bottleneck_block.tdf.1.running_var'] outputs: ['565']
|
||||
- [0] from output of MatMul_84 [name: 564]
|
||||
- [1] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.1.running_var]
|
||||
[086] Relu_86 type: Relu inputs: ['565'] outputs: ['566']
|
||||
- [0] from output of BatchNormalization_85 [name: 565]
|
||||
[087] MatMul_87 type: MatMul W inputs: ['566', '812'] outputs: ['568']
|
||||
- [0] from output of Relu_86 [name: 566]
|
||||
- [1] from weight of shape torch.Size([12, 96]) [name: 812]
|
||||
[088] BatchNormalization_88 type: BatchNormalization W inputs: ['568', 'bottleneck_block.tdf.4.weight', 'bottleneck_block.tdf.4.bias', 'bottleneck_block.tdf.4.running_mean', 'bottleneck_block.tdf.4.running_var'] outputs: ['569']
|
||||
- [0] from output of MatMul_87 [name: 568]
|
||||
- [1] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([288]) [name: bottleneck_block.tdf.4.running_var]
|
||||
[089] Relu_89 type: Relu inputs: ['569'] outputs: ['570']
|
||||
- [0] from output of BatchNormalization_88 [name: 569]
|
||||
[090] Add_90 type: Add inputs: ['562', '570'] outputs: ['571']
|
||||
- [0] from output of Relu_83 [name: 562]
|
||||
- [1] from output of Relu_89 [name: 570]
|
||||
[091] ConvTranspose_91 type: ConvTranspose W inputs: ['571', 'us.0.0.weight', 'us.0.0.bias'] outputs: ['572']
|
||||
- [0] from output of Add_90 [name: 571]
|
||||
- [1] from weight of shape torch.Size([288, 240, 2, 2]) [name: us.0.0.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: us.0.0.bias]
|
||||
[092] BatchNormalization_92 type: BatchNormalization W inputs: ['572', 'us.0.1.weight', 'us.0.1.bias', 'us.0.1.running_mean', 'us.0.1.running_var'] outputs: ['573']
|
||||
- [0] from output of ConvTranspose_91 [name: 572]
|
||||
- [1] from weight of shape torch.Size([240]) [name: us.0.1.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: us.0.1.bias]
|
||||
- [3] from weight of shape torch.Size([240]) [name: us.0.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([240]) [name: us.0.1.running_var]
|
||||
[093] Relu_93 type: Relu inputs: ['573'] outputs: ['574']
|
||||
- [0] from output of BatchNormalization_92 [name: 573]
|
||||
[094] Mul_94 type: Mul inputs: ['574', '550'] outputs: ['575']
|
||||
- [0] from output of Relu_93 [name: 574]
|
||||
- [1] from output of Add_75 [name: 550]
|
||||
[095] Conv_95 type: Conv W inputs: ['575', '757', '758'] outputs: ['756']
|
||||
- [0] from output of Mul_94 [name: 575]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 757]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 758]
|
||||
[096] Relu_96 type: Relu inputs: ['756'] outputs: ['578']
|
||||
- [0] from output of Conv_95 [name: 756]
|
||||
[097] Conv_97 type: Conv W inputs: ['578', '760', '761'] outputs: ['759']
|
||||
- [0] from output of Relu_96 [name: 578]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 760]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 761]
|
||||
[098] Relu_98 type: Relu inputs: ['759'] outputs: ['581']
|
||||
- [0] from output of Conv_97 [name: 759]
|
||||
[099] Conv_99 type: Conv W inputs: ['581', '763', '764'] outputs: ['762']
|
||||
- [0] from output of Relu_98 [name: 581]
|
||||
- [1] from weight of shape torch.Size([240, 240, 3, 3]) [name: 763]
|
||||
- [2] from weight of shape torch.Size([240]) [name: 764]
|
||||
[100] Relu_100 type: Relu inputs: ['762'] outputs: ['584']
|
||||
- [0] from output of Conv_99 [name: 762]
|
||||
[101] MatMul_101 type: MatMul W inputs: ['584', '813'] outputs: ['586']
|
||||
- [0] from output of Relu_100 [name: 584]
|
||||
- [1] from weight of shape torch.Size([192, 24]) [name: 813]
|
||||
[102] BatchNormalization_102 type: BatchNormalization W inputs: ['586', 'decoding_blocks.0.tdf.1.weight', 'decoding_blocks.0.tdf.1.bias', 'decoding_blocks.0.tdf.1.running_mean', 'decoding_blocks.0.tdf.1.running_var'] outputs: ['587']
|
||||
- [0] from output of MatMul_101 [name: 586]
|
||||
- [1] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.1.running_var]
|
||||
[103] Relu_103 type: Relu inputs: ['587'] outputs: ['588']
|
||||
- [0] from output of BatchNormalization_102 [name: 587]
|
||||
[104] MatMul_104 type: MatMul W inputs: ['588', '814'] outputs: ['590']
|
||||
- [0] from output of Relu_103 [name: 588]
|
||||
- [1] from weight of shape torch.Size([24, 192]) [name: 814]
|
||||
[105] BatchNormalization_105 type: BatchNormalization W inputs: ['590', 'decoding_blocks.0.tdf.4.weight', 'decoding_blocks.0.tdf.4.bias', 'decoding_blocks.0.tdf.4.running_mean', 'decoding_blocks.0.tdf.4.running_var'] outputs: ['591']
|
||||
- [0] from output of MatMul_104 [name: 590]
|
||||
- [1] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([240]) [name: decoding_blocks.0.tdf.4.running_var]
|
||||
[106] Relu_106 type: Relu inputs: ['591'] outputs: ['592']
|
||||
- [0] from output of BatchNormalization_105 [name: 591]
|
||||
[107] Add_107 type: Add inputs: ['584', '592'] outputs: ['593']
|
||||
- [0] from output of Relu_100 [name: 584]
|
||||
- [1] from output of Relu_106 [name: 592]
|
||||
[108] ConvTranspose_108 type: ConvTranspose W inputs: ['593', 'us.1.0.weight', 'us.1.0.bias'] outputs: ['594']
|
||||
- [0] from output of Add_107 [name: 593]
|
||||
- [1] from weight of shape torch.Size([240, 192, 2, 2]) [name: us.1.0.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: us.1.0.bias]
|
||||
[109] BatchNormalization_109 type: BatchNormalization W inputs: ['594', 'us.1.1.weight', 'us.1.1.bias', 'us.1.1.running_mean', 'us.1.1.running_var'] outputs: ['595']
|
||||
- [0] from output of ConvTranspose_108 [name: 594]
|
||||
- [1] from weight of shape torch.Size([192]) [name: us.1.1.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: us.1.1.bias]
|
||||
- [3] from weight of shape torch.Size([192]) [name: us.1.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([192]) [name: us.1.1.running_var]
|
||||
[110] Relu_110 type: Relu inputs: ['595'] outputs: ['596']
|
||||
- [0] from output of BatchNormalization_109 [name: 595]
|
||||
[111] Mul_111 type: Mul inputs: ['596', '529'] outputs: ['597']
|
||||
- [0] from output of Relu_110 [name: 596]
|
||||
- [1] from output of Add_60 [name: 529]
|
||||
[112] Conv_112 type: Conv W inputs: ['597', '766', '767'] outputs: ['765']
|
||||
- [0] from output of Mul_111 [name: 597]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 766]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 767]
|
||||
[113] Relu_113 type: Relu inputs: ['765'] outputs: ['600']
|
||||
- [0] from output of Conv_112 [name: 765]
|
||||
[114] Conv_114 type: Conv W inputs: ['600', '769', '770'] outputs: ['768']
|
||||
- [0] from output of Relu_113 [name: 600]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 769]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 770]
|
||||
[115] Relu_115 type: Relu inputs: ['768'] outputs: ['603']
|
||||
- [0] from output of Conv_114 [name: 768]
|
||||
[116] Conv_116 type: Conv W inputs: ['603', '772', '773'] outputs: ['771']
|
||||
- [0] from output of Relu_115 [name: 603]
|
||||
- [1] from weight of shape torch.Size([192, 192, 3, 3]) [name: 772]
|
||||
- [2] from weight of shape torch.Size([192]) [name: 773]
|
||||
[117] Relu_117 type: Relu inputs: ['771'] outputs: ['606']
|
||||
- [0] from output of Conv_116 [name: 771]
|
||||
[118] MatMul_118 type: MatMul W inputs: ['606', '815'] outputs: ['608']
|
||||
- [0] from output of Relu_117 [name: 606]
|
||||
- [1] from weight of shape torch.Size([384, 48]) [name: 815]
|
||||
[119] BatchNormalization_119 type: BatchNormalization W inputs: ['608', 'decoding_blocks.1.tdf.1.weight', 'decoding_blocks.1.tdf.1.bias', 'decoding_blocks.1.tdf.1.running_mean', 'decoding_blocks.1.tdf.1.running_var'] outputs: ['609']
|
||||
- [0] from output of MatMul_118 [name: 608]
|
||||
- [1] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.1.running_var]
|
||||
[120] Relu_120 type: Relu inputs: ['609'] outputs: ['610']
|
||||
- [0] from output of BatchNormalization_119 [name: 609]
|
||||
[121] MatMul_121 type: MatMul W inputs: ['610', '816'] outputs: ['612']
|
||||
- [0] from output of Relu_120 [name: 610]
|
||||
- [1] from weight of shape torch.Size([48, 384]) [name: 816]
|
||||
[122] BatchNormalization_122 type: BatchNormalization W inputs: ['612', 'decoding_blocks.1.tdf.4.weight', 'decoding_blocks.1.tdf.4.bias', 'decoding_blocks.1.tdf.4.running_mean', 'decoding_blocks.1.tdf.4.running_var'] outputs: ['613']
|
||||
- [0] from output of MatMul_121 [name: 612]
|
||||
- [1] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([192]) [name: decoding_blocks.1.tdf.4.running_var]
|
||||
[123] Relu_123 type: Relu inputs: ['613'] outputs: ['614']
|
||||
- [0] from output of BatchNormalization_122 [name: 613]
|
||||
[124] Add_124 type: Add inputs: ['606', '614'] outputs: ['615']
|
||||
- [0] from output of Relu_117 [name: 606]
|
||||
- [1] from output of Relu_123 [name: 614]
|
||||
[125] ConvTranspose_125 type: ConvTranspose W inputs: ['615', 'us.2.0.weight', 'us.2.0.bias'] outputs: ['616']
|
||||
- [0] from output of Add_124 [name: 615]
|
||||
- [1] from weight of shape torch.Size([192, 144, 2, 2]) [name: us.2.0.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: us.2.0.bias]
|
||||
[126] BatchNormalization_126 type: BatchNormalization W inputs: ['616', 'us.2.1.weight', 'us.2.1.bias', 'us.2.1.running_mean', 'us.2.1.running_var'] outputs: ['617']
|
||||
- [0] from output of ConvTranspose_125 [name: 616]
|
||||
- [1] from weight of shape torch.Size([144]) [name: us.2.1.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: us.2.1.bias]
|
||||
- [3] from weight of shape torch.Size([144]) [name: us.2.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([144]) [name: us.2.1.running_var]
|
||||
[127] Relu_127 type: Relu inputs: ['617'] outputs: ['618']
|
||||
- [0] from output of BatchNormalization_126 [name: 617]
|
||||
[128] Mul_128 type: Mul inputs: ['618', '508'] outputs: ['619']
|
||||
- [0] from output of Relu_127 [name: 618]
|
||||
- [1] from output of Add_45 [name: 508]
|
||||
[129] Conv_129 type: Conv W inputs: ['619', '775', '776'] outputs: ['774']
|
||||
- [0] from output of Mul_128 [name: 619]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 775]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 776]
|
||||
[130] Relu_130 type: Relu inputs: ['774'] outputs: ['622']
|
||||
- [0] from output of Conv_129 [name: 774]
|
||||
[131] Conv_131 type: Conv W inputs: ['622', '778', '779'] outputs: ['777']
|
||||
- [0] from output of Relu_130 [name: 622]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 778]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 779]
|
||||
[132] Relu_132 type: Relu inputs: ['777'] outputs: ['625']
|
||||
- [0] from output of Conv_131 [name: 777]
|
||||
[133] Conv_133 type: Conv W inputs: ['625', '781', '782'] outputs: ['780']
|
||||
- [0] from output of Relu_132 [name: 625]
|
||||
- [1] from weight of shape torch.Size([144, 144, 3, 3]) [name: 781]
|
||||
- [2] from weight of shape torch.Size([144]) [name: 782]
|
||||
[134] Relu_134 type: Relu inputs: ['780'] outputs: ['628']
|
||||
- [0] from output of Conv_133 [name: 780]
|
||||
[135] MatMul_135 type: MatMul W inputs: ['628', '817'] outputs: ['630']
|
||||
- [0] from output of Relu_134 [name: 628]
|
||||
- [1] from weight of shape torch.Size([768, 96]) [name: 817]
|
||||
[136] BatchNormalization_136 type: BatchNormalization W inputs: ['630', 'decoding_blocks.2.tdf.1.weight', 'decoding_blocks.2.tdf.1.bias', 'decoding_blocks.2.tdf.1.running_mean', 'decoding_blocks.2.tdf.1.running_var'] outputs: ['631']
|
||||
- [0] from output of MatMul_135 [name: 630]
|
||||
- [1] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.1.running_var]
|
||||
[137] Relu_137 type: Relu inputs: ['631'] outputs: ['632']
|
||||
- [0] from output of BatchNormalization_136 [name: 631]
|
||||
[138] MatMul_138 type: MatMul W inputs: ['632', '818'] outputs: ['634']
|
||||
- [0] from output of Relu_137 [name: 632]
|
||||
- [1] from weight of shape torch.Size([96, 768]) [name: 818]
|
||||
[139] BatchNormalization_139 type: BatchNormalization W inputs: ['634', 'decoding_blocks.2.tdf.4.weight', 'decoding_blocks.2.tdf.4.bias', 'decoding_blocks.2.tdf.4.running_mean', 'decoding_blocks.2.tdf.4.running_var'] outputs: ['635']
|
||||
- [0] from output of MatMul_138 [name: 634]
|
||||
- [1] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([144]) [name: decoding_blocks.2.tdf.4.running_var]
|
||||
[140] Relu_140 type: Relu inputs: ['635'] outputs: ['636']
|
||||
- [0] from output of BatchNormalization_139 [name: 635]
|
||||
[141] Add_141 type: Add inputs: ['628', '636'] outputs: ['637']
|
||||
- [0] from output of Relu_134 [name: 628]
|
||||
- [1] from output of Relu_140 [name: 636]
|
||||
[142] ConvTranspose_142 type: ConvTranspose W inputs: ['637', 'us.3.0.weight', 'us.3.0.bias'] outputs: ['638']
|
||||
- [0] from output of Add_141 [name: 637]
|
||||
- [1] from weight of shape torch.Size([144, 96, 2, 2]) [name: us.3.0.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: us.3.0.bias]
|
||||
[143] BatchNormalization_143 type: BatchNormalization W inputs: ['638', 'us.3.1.weight', 'us.3.1.bias', 'us.3.1.running_mean', 'us.3.1.running_var'] outputs: ['639']
|
||||
- [0] from output of ConvTranspose_142 [name: 638]
|
||||
- [1] from weight of shape torch.Size([96]) [name: us.3.1.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: us.3.1.bias]
|
||||
- [3] from weight of shape torch.Size([96]) [name: us.3.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([96]) [name: us.3.1.running_var]
|
||||
[144] Relu_144 type: Relu inputs: ['639'] outputs: ['640']
|
||||
- [0] from output of BatchNormalization_143 [name: 639]
|
||||
[145] Mul_145 type: Mul inputs: ['640', '487'] outputs: ['641']
|
||||
- [0] from output of Relu_144 [name: 640]
|
||||
- [1] from output of Add_30 [name: 487]
|
||||
[146] Conv_146 type: Conv W inputs: ['641', '784', '785'] outputs: ['783']
|
||||
- [0] from output of Mul_145 [name: 641]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 784]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 785]
|
||||
[147] Relu_147 type: Relu inputs: ['783'] outputs: ['644']
|
||||
- [0] from output of Conv_146 [name: 783]
|
||||
[148] Conv_148 type: Conv W inputs: ['644', '787', '788'] outputs: ['786']
|
||||
- [0] from output of Relu_147 [name: 644]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 787]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 788]
|
||||
[149] Relu_149 type: Relu inputs: ['786'] outputs: ['647']
|
||||
- [0] from output of Conv_148 [name: 786]
|
||||
[150] Conv_150 type: Conv W inputs: ['647', '790', '791'] outputs: ['789']
|
||||
- [0] from output of Relu_149 [name: 647]
|
||||
- [1] from weight of shape torch.Size([96, 96, 3, 3]) [name: 790]
|
||||
- [2] from weight of shape torch.Size([96]) [name: 791]
|
||||
[151] Relu_151 type: Relu inputs: ['789'] outputs: ['650']
|
||||
- [0] from output of Conv_150 [name: 789]
|
||||
[152] MatMul_152 type: MatMul W inputs: ['650', '819'] outputs: ['652']
|
||||
- [0] from output of Relu_151 [name: 650]
|
||||
- [1] from weight of shape torch.Size([1536, 192]) [name: 819]
|
||||
[153] BatchNormalization_153 type: BatchNormalization W inputs: ['652', 'decoding_blocks.3.tdf.1.weight', 'decoding_blocks.3.tdf.1.bias', 'decoding_blocks.3.tdf.1.running_mean', 'decoding_blocks.3.tdf.1.running_var'] outputs: ['653']
|
||||
- [0] from output of MatMul_152 [name: 652]
|
||||
- [1] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.1.running_var]
|
||||
[154] Relu_154 type: Relu inputs: ['653'] outputs: ['654']
|
||||
- [0] from output of BatchNormalization_153 [name: 653]
|
||||
[155] MatMul_155 type: MatMul W inputs: ['654', '820'] outputs: ['656']
|
||||
- [0] from output of Relu_154 [name: 654]
|
||||
- [1] from weight of shape torch.Size([192, 1536]) [name: 820]
|
||||
[156] BatchNormalization_156 type: BatchNormalization W inputs: ['656', 'decoding_blocks.3.tdf.4.weight', 'decoding_blocks.3.tdf.4.bias', 'decoding_blocks.3.tdf.4.running_mean', 'decoding_blocks.3.tdf.4.running_var'] outputs: ['657']
|
||||
- [0] from output of MatMul_155 [name: 656]
|
||||
- [1] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([96]) [name: decoding_blocks.3.tdf.4.running_var]
|
||||
[157] Relu_157 type: Relu inputs: ['657'] outputs: ['658']
|
||||
- [0] from output of BatchNormalization_156 [name: 657]
|
||||
[158] Add_158 type: Add inputs: ['650', '658'] outputs: ['659']
|
||||
- [0] from output of Relu_151 [name: 650]
|
||||
- [1] from output of Relu_157 [name: 658]
|
||||
[159] ConvTranspose_159 type: ConvTranspose W inputs: ['659', 'us.4.0.weight', 'us.4.0.bias'] outputs: ['660']
|
||||
- [0] from output of Add_158 [name: 659]
|
||||
- [1] from weight of shape torch.Size([96, 48, 2, 2]) [name: us.4.0.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: us.4.0.bias]
|
||||
[160] BatchNormalization_160 type: BatchNormalization W inputs: ['660', 'us.4.1.weight', 'us.4.1.bias', 'us.4.1.running_mean', 'us.4.1.running_var'] outputs: ['661']
|
||||
- [0] from output of ConvTranspose_159 [name: 660]
|
||||
- [1] from weight of shape torch.Size([48]) [name: us.4.1.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: us.4.1.bias]
|
||||
- [3] from weight of shape torch.Size([48]) [name: us.4.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([48]) [name: us.4.1.running_var]
|
||||
[161] Relu_161 type: Relu inputs: ['661'] outputs: ['662']
|
||||
- [0] from output of BatchNormalization_160 [name: 661]
|
||||
[162] Mul_162 type: Mul inputs: ['662', '466'] outputs: ['663']
|
||||
- [0] from output of Relu_161 [name: 662]
|
||||
- [1] from output of Add_15 [name: 466]
|
||||
[163] Conv_163 type: Conv W inputs: ['663', '793', '794'] outputs: ['792']
|
||||
- [0] from output of Mul_162 [name: 663]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 793]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 794]
|
||||
[164] Relu_164 type: Relu inputs: ['792'] outputs: ['666']
|
||||
- [0] from output of Conv_163 [name: 792]
|
||||
[165] Conv_165 type: Conv W inputs: ['666', '796', '797'] outputs: ['795']
|
||||
- [0] from output of Relu_164 [name: 666]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 796]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 797]
|
||||
[166] Relu_166 type: Relu inputs: ['795'] outputs: ['669']
|
||||
- [0] from output of Conv_165 [name: 795]
|
||||
[167] Conv_167 type: Conv W inputs: ['669', '799', '800'] outputs: ['798']
|
||||
- [0] from output of Relu_166 [name: 669]
|
||||
- [1] from weight of shape torch.Size([48, 48, 3, 3]) [name: 799]
|
||||
- [2] from weight of shape torch.Size([48]) [name: 800]
|
||||
[168] Relu_168 type: Relu inputs: ['798'] outputs: ['672']
|
||||
- [0] from output of Conv_167 [name: 798]
|
||||
[169] MatMul_169 type: MatMul W inputs: ['672', '821'] outputs: ['674']
|
||||
- [0] from output of Relu_168 [name: 672]
|
||||
- [1] from weight of shape torch.Size([3072, 384]) [name: 821]
|
||||
[170] BatchNormalization_170 type: BatchNormalization W inputs: ['674', 'decoding_blocks.4.tdf.1.weight', 'decoding_blocks.4.tdf.1.bias', 'decoding_blocks.4.tdf.1.running_mean', 'decoding_blocks.4.tdf.1.running_var'] outputs: ['675']
|
||||
- [0] from output of MatMul_169 [name: 674]
|
||||
- [1] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.1.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.1.bias]
|
||||
- [3] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.1.running_mean]
|
||||
- [4] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.1.running_var]
|
||||
[171] Relu_171 type: Relu inputs: ['675'] outputs: ['676']
|
||||
- [0] from output of BatchNormalization_170 [name: 675]
|
||||
[172] MatMul_172 type: MatMul W inputs: ['676', '822'] outputs: ['678']
|
||||
- [0] from output of Relu_171 [name: 676]
|
||||
- [1] from weight of shape torch.Size([384, 3072]) [name: 822]
|
||||
[173] BatchNormalization_173 type: BatchNormalization W inputs: ['678', 'decoding_blocks.4.tdf.4.weight', 'decoding_blocks.4.tdf.4.bias', 'decoding_blocks.4.tdf.4.running_mean', 'decoding_blocks.4.tdf.4.running_var'] outputs: ['679']
|
||||
- [0] from output of MatMul_172 [name: 678]
|
||||
- [1] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.4.weight]
|
||||
- [2] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.4.bias]
|
||||
- [3] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.4.running_mean]
|
||||
- [4] from weight of shape torch.Size([48]) [name: decoding_blocks.4.tdf.4.running_var]
|
||||
[174] Relu_174 type: Relu inputs: ['679'] outputs: ['680']
|
||||
- [0] from output of BatchNormalization_173 [name: 679]
|
||||
[175] Add_175 type: Add inputs: ['672', '680'] outputs: ['681']
|
||||
- [0] from output of Relu_168 [name: 672]
|
||||
- [1] from output of Relu_174 [name: 680]
|
||||
[176] Transpose_176 type: Transpose inputs: ['681'] outputs: ['682']
|
||||
- [0] from output of Add_175 [name: 681]
|
||||
[177] Conv_177 type: Conv W inputs: ['682', 'final_conv.0.weight', 'final_conv.0.bias'] outputs: ['output']
|
||||
- [0] from output of Transpose_176 [name: 682]
|
||||
- [1] from weight of shape torch.Size([4, 48, 1, 1]) [name: final_conv.0.weight]
|
||||
- [2] from weight of shape torch.Size([4]) [name: final_conv.0.bias]
|
||||
The output is from Conv_177
|
||||
@@ -0,0 +1,232 @@
|
||||
('initial_conv.weight', '685', False),
|
||||
('initial_conv.bias', '686', False),
|
||||
# Encoder Stage 1
|
||||
('enc_stage_1.time_branch.sequence.0.weight', '688', False),
|
||||
('enc_stage_1.time_branch.sequence.0.bias', '689', False),
|
||||
('enc_stage_1.time_branch.sequence.2.weight', '691', False),
|
||||
('enc_stage_1.time_branch.sequence.2.bias', '692', False),
|
||||
('enc_stage_1.time_branch.sequence.4.weight', '694', False),
|
||||
('enc_stage_1.time_branch.sequence.4.bias', '695', False),
|
||||
('enc_stage_1.freq_branch.sequence.0.weight', '801', True),
|
||||
('enc_stage_1.freq_branch.sequence.1.weight', 'encoding_blocks.0.tdf.1.weight', False),
|
||||
('enc_stage_1.freq_branch.sequence.1.bias', 'encoding_blocks.0.tdf.1.bias', False),
|
||||
('enc_stage_1.freq_branch.sequence.1.running_mean', 'encoding_blocks.0.tdf.1.running_mean', False),
|
||||
('enc_stage_1.freq_branch.sequence.1.running_var', 'encoding_blocks.0.tdf.1.running_var', False),
|
||||
('enc_stage_1.freq_branch.sequence.3.weight', '802', True),
|
||||
('enc_stage_1.freq_branch.sequence.4.weight', 'encoding_blocks.0.tdf.4.weight', False),
|
||||
('enc_stage_1.freq_branch.sequence.4.bias', 'encoding_blocks.0.tdf.4.bias', False),
|
||||
('enc_stage_1.freq_branch.sequence.4.running_mean', 'encoding_blocks.0.tdf.4.running_mean', False),
|
||||
('enc_stage_1.freq_branch.sequence.4.running_var', 'encoding_blocks.0.tdf.4.running_var', False),
|
||||
('enc_down_1.0.weight', '697', False),
|
||||
('enc_down_1.0.bias', '698', False),
|
||||
# Encoder Stage 2
|
||||
('enc_stage_2.time_branch.sequence.0.weight', '700', False),
|
||||
('enc_stage_2.time_branch.sequence.0.bias', '701', False),
|
||||
('enc_stage_2.time_branch.sequence.2.weight', '703', False),
|
||||
('enc_stage_2.time_branch.sequence.2.bias', '704', False),
|
||||
('enc_stage_2.time_branch.sequence.4.weight', '706', False),
|
||||
('enc_stage_2.time_branch.sequence.4.bias', '707', False),
|
||||
('enc_stage_2.freq_branch.sequence.0.weight', '803', True),
|
||||
('enc_stage_2.freq_branch.sequence.1.weight', 'encoding_blocks.1.tdf.1.weight', False),
|
||||
('enc_stage_2.freq_branch.sequence.1.bias', 'encoding_blocks.1.tdf.1.bias', False),
|
||||
('enc_stage_2.freq_branch.sequence.1.running_mean', 'encoding_blocks.1.tdf.1.running_mean', False),
|
||||
('enc_stage_2.freq_branch.sequence.1.running_var', 'encoding_blocks.1.tdf.1.running_var', False),
|
||||
('enc_stage_2.freq_branch.sequence.3.weight', '804', True),
|
||||
('enc_stage_2.freq_branch.sequence.4.weight', 'encoding_blocks.1.tdf.4.weight', False),
|
||||
('enc_stage_2.freq_branch.sequence.4.bias', 'encoding_blocks.1.tdf.4.bias', False),
|
||||
('enc_stage_2.freq_branch.sequence.4.running_mean', 'encoding_blocks.1.tdf.4.running_mean', False),
|
||||
('enc_stage_2.freq_branch.sequence.4.running_var', 'encoding_blocks.1.tdf.4.running_var', False),
|
||||
('enc_down_2.0.weight', '709', False),
|
||||
('enc_down_2.0.bias', '710', False),
|
||||
# Encoder Stage 3
|
||||
('enc_stage_3.time_branch.sequence.0.weight', '712', False),
|
||||
('enc_stage_3.time_branch.sequence.0.bias', '713', False),
|
||||
('enc_stage_3.time_branch.sequence.2.weight', '715', False),
|
||||
('enc_stage_3.time_branch.sequence.2.bias', '716', False),
|
||||
('enc_stage_3.time_branch.sequence.4.weight', '718', False),
|
||||
('enc_stage_3.time_branch.sequence.4.bias', '719', False),
|
||||
('enc_stage_3.freq_branch.sequence.0.weight', '805', True),
|
||||
('enc_stage_3.freq_branch.sequence.1.weight', 'encoding_blocks.2.tdf.1.weight', False),
|
||||
('enc_stage_3.freq_branch.sequence.1.bias', 'encoding_blocks.2.tdf.1.bias', False),
|
||||
('enc_stage_3.freq_branch.sequence.1.running_mean', 'encoding_blocks.2.tdf.1.running_mean', False),
|
||||
('enc_stage_3.freq_branch.sequence.1.running_var', 'encoding_blocks.2.tdf.1.running_var', False),
|
||||
('enc_stage_3.freq_branch.sequence.3.weight', '806', True),
|
||||
('enc_stage_3.freq_branch.sequence.4.weight', 'encoding_blocks.2.tdf.4.weight', False),
|
||||
('enc_stage_3.freq_branch.sequence.4.bias', 'encoding_blocks.2.tdf.4.bias', False),
|
||||
('enc_stage_3.freq_branch.sequence.4.running_mean', 'encoding_blocks.2.tdf.4.running_mean', False),
|
||||
('enc_stage_3.freq_branch.sequence.4.running_var', 'encoding_blocks.2.tdf.4.running_var', False),
|
||||
('enc_down_3.0.weight', '721', False),
|
||||
('enc_down_3.0.bias', '722', False),
|
||||
# Encoder Stage 4
|
||||
('enc_stage_4.time_branch.sequence.0.weight', '724', False),
|
||||
('enc_stage_4.time_branch.sequence.0.bias', '725', False),
|
||||
('enc_stage_4.time_branch.sequence.2.weight', '727', False),
|
||||
('enc_stage_4.time_branch.sequence.2.bias', '728', False),
|
||||
('enc_stage_4.time_branch.sequence.4.weight', '730', False),
|
||||
('enc_stage_4.time_branch.sequence.4.bias', '731', False),
|
||||
('enc_stage_4.freq_branch.sequence.0.weight', '807', True),
|
||||
('enc_stage_4.freq_branch.sequence.1.weight', 'encoding_blocks.3.tdf.1.weight', False),
|
||||
('enc_stage_4.freq_branch.sequence.1.bias', 'encoding_blocks.3.tdf.1.bias', False),
|
||||
('enc_stage_4.freq_branch.sequence.1.running_mean', 'encoding_blocks.3.tdf.1.running_mean', False),
|
||||
('enc_stage_4.freq_branch.sequence.1.running_var', 'encoding_blocks.3.tdf.1.running_var', False),
|
||||
('enc_stage_4.freq_branch.sequence.3.weight', '808', True),
|
||||
('enc_stage_4.freq_branch.sequence.4.weight', 'encoding_blocks.3.tdf.4.weight', False),
|
||||
('enc_stage_4.freq_branch.sequence.4.bias', 'encoding_blocks.3.tdf.4.bias', False),
|
||||
('enc_stage_4.freq_branch.sequence.4.running_mean', 'encoding_blocks.3.tdf.4.running_mean', False),
|
||||
('enc_stage_4.freq_branch.sequence.4.running_var', 'encoding_blocks.3.tdf.4.running_var', False),
|
||||
('enc_down_4.0.weight', '733', False),
|
||||
('enc_down_4.0.bias', '734', False),
|
||||
# Encoder Stage 5
|
||||
('enc_stage_5.time_branch.sequence.0.weight', '736', False),
|
||||
('enc_stage_5.time_branch.sequence.0.bias', '737', False),
|
||||
('enc_stage_5.time_branch.sequence.2.weight', '739', False),
|
||||
('enc_stage_5.time_branch.sequence.2.bias', '740', False),
|
||||
('enc_stage_5.time_branch.sequence.4.weight', '742', False),
|
||||
('enc_stage_5.time_branch.sequence.4.bias', '743', False),
|
||||
('enc_stage_5.freq_branch.sequence.0.weight', '809', True),
|
||||
('enc_stage_5.freq_branch.sequence.1.weight', 'encoding_blocks.4.tdf.1.weight', False),
|
||||
('enc_stage_5.freq_branch.sequence.1.bias', 'encoding_blocks.4.tdf.1.bias', False),
|
||||
('enc_stage_5.freq_branch.sequence.1.running_mean', 'encoding_blocks.4.tdf.1.running_mean', False),
|
||||
('enc_stage_5.freq_branch.sequence.1.running_var', 'encoding_blocks.4.tdf.1.running_var', False),
|
||||
('enc_stage_5.freq_branch.sequence.3.weight', '810', True),
|
||||
('enc_stage_5.freq_branch.sequence.4.weight', 'encoding_blocks.4.tdf.4.weight', False),
|
||||
('enc_stage_5.freq_branch.sequence.4.bias', 'encoding_blocks.4.tdf.4.bias', False),
|
||||
('enc_stage_5.freq_branch.sequence.4.running_mean', 'encoding_blocks.4.tdf.4.running_mean', False),
|
||||
('enc_stage_5.freq_branch.sequence.4.running_var', 'encoding_blocks.4.tdf.4.running_var', False),
|
||||
('enc_down_5.0.weight', '745', False),
|
||||
('enc_down_5.0.bias', '746', False),
|
||||
# Bottleneck
|
||||
('bottleneck.time_branch.sequence.0.weight', '748', False),
|
||||
('bottleneck.time_branch.sequence.0.bias', '749', False),
|
||||
('bottleneck.time_branch.sequence.2.weight', '751', False),
|
||||
('bottleneck.time_branch.sequence.2.bias', '752', False),
|
||||
('bottleneck.time_branch.sequence.4.weight', '754', False),
|
||||
('bottleneck.time_branch.sequence.4.bias', '755', False),
|
||||
('bottleneck.freq_branch.sequence.0.weight', '811', True),
|
||||
('bottleneck.freq_branch.sequence.1.weight', 'bottleneck_block.tdf.1.weight', False),
|
||||
('bottleneck.freq_branch.sequence.1.bias', 'bottleneck_block.tdf.1.bias', False),
|
||||
('bottleneck.freq_branch.sequence.1.running_mean', 'bottleneck_block.tdf.1.running_mean', False),
|
||||
('bottleneck.freq_branch.sequence.1.running_var', 'bottleneck_block.tdf.1.running_var', False),
|
||||
('bottleneck.freq_branch.sequence.3.weight', '812', True),
|
||||
('bottleneck.freq_branch.sequence.4.weight', 'bottleneck_block.tdf.4.weight', False),
|
||||
('bottleneck.freq_branch.sequence.4.bias', 'bottleneck_block.tdf.4.bias', False),
|
||||
('bottleneck.freq_branch.sequence.4.running_mean', 'bottleneck_block.tdf.4.running_mean', False),
|
||||
('bottleneck.freq_branch.sequence.4.running_var', 'bottleneck_block.tdf.4.running_var', False),
|
||||
# Decoder Stage 1
|
||||
('dec_up_1.0.weight', 'us.0.0.weight', False),
|
||||
('dec_up_1.0.bias', 'us.0.0.bias', False),
|
||||
('dec_up_1.1.weight', 'us.0.1.weight', False),
|
||||
('dec_up_1.1.bias', 'us.0.1.bias', False),
|
||||
('dec_up_1.1.running_mean', 'us.0.1.running_mean', False),
|
||||
('dec_up_1.1.running_var', 'us.0.1.running_var', False),
|
||||
('dec_stage_1.time_branch.sequence.0.weight', '757', False),
|
||||
('dec_stage_1.time_branch.sequence.0.bias', '758', False),
|
||||
('dec_stage_1.time_branch.sequence.2.weight', '760', False),
|
||||
('dec_stage_1.time_branch.sequence.2.bias', '761', False),
|
||||
('dec_stage_1.time_branch.sequence.4.weight', '763', False),
|
||||
('dec_stage_1.time_branch.sequence.4.bias', '764', False),
|
||||
('dec_stage_1.freq_branch.sequence.0.weight', '813', True),
|
||||
('dec_stage_1.freq_branch.sequence.1.weight', 'decoding_blocks.0.tdf.1.weight', False),
|
||||
('dec_stage_1.freq_branch.sequence.1.bias', 'decoding_blocks.0.tdf.1.bias', False),
|
||||
('dec_stage_1.freq_branch.sequence.1.running_mean', 'decoding_blocks.0.tdf.1.running_mean', False),
|
||||
('dec_stage_1.freq_branch.sequence.1.running_var', 'decoding_blocks.0.tdf.1.running_var', False),
|
||||
('dec_stage_1.freq_branch.sequence.3.weight', '814', True),
|
||||
('dec_stage_1.freq_branch.sequence.4.weight', 'decoding_blocks.0.tdf.4.weight', False),
|
||||
('dec_stage_1.freq_branch.sequence.4.bias', 'decoding_blocks.0.tdf.4.bias', False),
|
||||
('dec_stage_1.freq_branch.sequence.4.running_mean', 'decoding_blocks.0.tdf.4.running_mean', False),
|
||||
('dec_stage_1.freq_branch.sequence.4.running_var', 'decoding_blocks.0.tdf.4.running_var', False),
|
||||
# Decoder Stage 2
|
||||
('dec_up_2.0.weight', 'us.1.0.weight', False),
|
||||
('dec_up_2.0.bias', 'us.1.0.bias', False),
|
||||
('dec_up_2.1.weight', 'us.1.1.weight', False),
|
||||
('dec_up_2.1.bias', 'us.1.1.bias', False),
|
||||
('dec_up_2.1.running_mean', 'us.1.1.running_mean', False),
|
||||
('dec_up_2.1.running_var', 'us.1.1.running_var', False),
|
||||
('dec_stage_2.time_branch.sequence.0.weight', '766', False),
|
||||
('dec_stage_2.time_branch.sequence.0.bias', '767', False),
|
||||
('dec_stage_2.time_branch.sequence.2.weight', '769', False),
|
||||
('dec_stage_2.time_branch.sequence.2.bias', '770', False),
|
||||
('dec_stage_2.time_branch.sequence.4.weight', '772', False),
|
||||
('dec_stage_2.time_branch.sequence.4.bias', '773', False),
|
||||
('dec_stage_2.freq_branch.sequence.0.weight', '815', True),
|
||||
('dec_stage_2.freq_branch.sequence.1.weight', 'decoding_blocks.1.tdf.1.weight', False),
|
||||
('dec_stage_2.freq_branch.sequence.1.bias', 'decoding_blocks.1.tdf.1.bias', False),
|
||||
('dec_stage_2.freq_branch.sequence.1.running_mean', 'decoding_blocks.1.tdf.1.running_mean', False),
|
||||
('dec_stage_2.freq_branch.sequence.1.running_var', 'decoding_blocks.1.tdf.1.running_var', False),
|
||||
('dec_stage_2.freq_branch.sequence.3.weight', '816', True),
|
||||
('dec_stage_2.freq_branch.sequence.4.weight', 'decoding_blocks.1.tdf.4.weight', False),
|
||||
('dec_stage_2.freq_branch.sequence.4.bias', 'decoding_blocks.1.tdf.4.bias', False),
|
||||
('dec_stage_2.freq_branch.sequence.4.running_mean', 'decoding_blocks.1.tdf.4.running_mean', False),
|
||||
('dec_stage_2.freq_branch.sequence.4.running_var', 'decoding_blocks.1.tdf.4.running_var', False),
|
||||
# Decoder Stage 3
|
||||
('dec_up_3.0.weight', 'us.2.0.weight', False),
|
||||
('dec_up_3.0.bias', 'us.2.0.bias', False),
|
||||
('dec_up_3.1.weight', 'us.2.1.weight', False),
|
||||
('dec_up_3.1.bias', 'us.2.1.bias', False),
|
||||
('dec_up_3.1.running_mean', 'us.2.1.running_mean', False),
|
||||
('dec_up_3.1.running_var', 'us.2.1.running_var', False),
|
||||
('dec_stage_3.time_branch.sequence.0.weight', '775', False),
|
||||
('dec_stage_3.time_branch.sequence.0.bias', '776', False),
|
||||
('dec_stage_3.time_branch.sequence.2.weight', '778', False),
|
||||
('dec_stage_3.time_branch.sequence.2.bias', '779', False),
|
||||
('dec_stage_3.time_branch.sequence.4.weight', '781', False),
|
||||
('dec_stage_3.time_branch.sequence.4.bias', '782', False),
|
||||
('dec_stage_3.freq_branch.sequence.0.weight', '817', True),
|
||||
('dec_stage_3.freq_branch.sequence.1.weight', 'decoding_blocks.2.tdf.1.weight', False),
|
||||
('dec_stage_3.freq_branch.sequence.1.bias', 'decoding_blocks.2.tdf.1.bias', False),
|
||||
('dec_stage_3.freq_branch.sequence.1.running_mean', 'decoding_blocks.2.tdf.1.running_mean', False),
|
||||
('dec_stage_3.freq_branch.sequence.1.running_var', 'decoding_blocks.2.tdf.1.running_var', False),
|
||||
('dec_stage_3.freq_branch.sequence.3.weight', '818', True),
|
||||
('dec_stage_3.freq_branch.sequence.4.weight', 'decoding_blocks.2.tdf.4.weight', False),
|
||||
('dec_stage_3.freq_branch.sequence.4.bias', 'decoding_blocks.2.tdf.4.bias', False),
|
||||
('dec_stage_3.freq_branch.sequence.4.running_mean', 'decoding_blocks.2.tdf.4.running_mean', False),
|
||||
('dec_stage_3.freq_branch.sequence.4.running_var', 'decoding_blocks.2.tdf.4.running_var', False),
|
||||
# Decoder Stage 4
|
||||
('dec_up_4.0.weight', 'us.3.0.weight', False),
|
||||
('dec_up_4.0.bias', 'us.3.0.bias', False),
|
||||
('dec_up_4.1.weight', 'us.3.1.weight', False),
|
||||
('dec_up_4.1.bias', 'us.3.1.bias', False),
|
||||
('dec_up_4.1.running_mean', 'us.3.1.running_mean', False),
|
||||
('dec_up_4.1.running_var', 'us.3.1.running_var', False),
|
||||
('dec_stage_4.time_branch.sequence.0.weight', '784', False),
|
||||
('dec_stage_4.time_branch.sequence.0.bias', '785', False),
|
||||
('dec_stage_4.time_branch.sequence.2.weight', '787', False),
|
||||
('dec_stage_4.time_branch.sequence.2.bias', '788', False),
|
||||
('dec_stage_4.time_branch.sequence.4.weight', '790', False),
|
||||
('dec_stage_4.time_branch.sequence.4.bias', '791', False),
|
||||
('dec_stage_4.freq_branch.sequence.0.weight', '819', True),
|
||||
('dec_stage_4.freq_branch.sequence.1.weight', 'decoding_blocks.3.tdf.1.weight', False),
|
||||
('dec_stage_4.freq_branch.sequence.1.bias', 'decoding_blocks.3.tdf.1.bias', False),
|
||||
('dec_stage_4.freq_branch.sequence.1.running_mean', 'decoding_blocks.3.tdf.1.running_mean', False),
|
||||
('dec_stage_4.freq_branch.sequence.1.running_var', 'decoding_blocks.3.tdf.1.running_var', False),
|
||||
('dec_stage_4.freq_branch.sequence.3.weight', '820', True),
|
||||
('dec_stage_4.freq_branch.sequence.4.weight', 'decoding_blocks.3.tdf.4.weight', False),
|
||||
('dec_stage_4.freq_branch.sequence.4.bias', 'decoding_blocks.3.tdf.4.bias', False),
|
||||
('dec_stage_4.freq_branch.sequence.4.running_mean', 'decoding_blocks.3.tdf.4.running_mean', False),
|
||||
('dec_stage_4.freq_branch.sequence.4.running_var', 'decoding_blocks.3.tdf.4.running_var', False),
|
||||
# Decoder Stage 5
|
||||
('dec_up_5.0.weight', 'us.4.0.weight', False),
|
||||
('dec_up_5.0.bias', 'us.4.0.bias', False),
|
||||
('dec_up_5.1.weight', 'us.4.1.weight', False),
|
||||
('dec_up_5.1.bias', 'us.4.1.bias', False),
|
||||
('dec_up_5.1.running_mean', 'us.4.1.running_mean', False),
|
||||
('dec_up_5.1.running_var', 'us.4.1.running_var', False),
|
||||
('dec_stage_5.time_branch.sequence.0.weight', '793', False),
|
||||
('dec_stage_5.time_branch.sequence.0.bias', '794', False),
|
||||
('dec_stage_5.time_branch.sequence.2.weight', '796', False),
|
||||
('dec_stage_5.time_branch.sequence.2.bias', '797', False),
|
||||
('dec_stage_5.time_branch.sequence.4.weight', '799', False),
|
||||
('dec_stage_5.time_branch.sequence.4.bias', '800', False),
|
||||
('dec_stage_5.freq_branch.sequence.0.weight', '821', True),
|
||||
('dec_stage_5.freq_branch.sequence.1.weight', 'decoding_blocks.4.tdf.1.weight', False),
|
||||
('dec_stage_5.freq_branch.sequence.1.bias', 'decoding_blocks.4.tdf.1.bias', False),
|
||||
('dec_stage_5.freq_branch.sequence.1.running_mean', 'decoding_blocks.4.tdf.1.running_mean', False),
|
||||
('dec_stage_5.freq_branch.sequence.1.running_var', 'decoding_blocks.4.tdf.1.running_var', False),
|
||||
('dec_stage_5.freq_branch.sequence.3.weight', '822', True),
|
||||
('dec_stage_5.freq_branch.sequence.4.weight', 'decoding_blocks.4.tdf.4.weight', False),
|
||||
('dec_stage_5.freq_branch.sequence.4.bias', 'decoding_blocks.4.tdf.4.bias', False),
|
||||
('dec_stage_5.freq_branch.sequence.4.running_mean', 'decoding_blocks.4.tdf.4.running_mean', False),
|
||||
('dec_stage_5.freq_branch.sequence.4.running_var', 'decoding_blocks.4.tdf.4.running_var', False),
|
||||
# Final Layer
|
||||
('final_conv.weight', 'final_conv.0.weight', False),
|
||||
('final_conv.bias', 'final_conv.0.bias', False),
|
||||
@@ -0,0 +1,16 @@
|
||||
# MDX-Net
|
||||
|
||||
- [RVC Studio](https://github.com/SayanoAI/RVC-Studio): has a [HuggingFace repo](https://huggingface.co/datasets/SayanoAI/RVC-Studio)
|
||||
- [Kim Vocals 2](https://huggingface.co/datasets/SayanoAI/RVC-Studio/resolve/main/MDXNET/Kim_Vocal_2.onnx?download=true)
|
||||
- [UVR-MDX-NET-vocal_FT](https://huggingface.co/datasets/SayanoAI/RVC-Studio/resolve/main/MDXNET/UVR-MDX-NET-vocal_FT.onnx?download=true)
|
||||
- Contains other UVR files
|
||||
- MIT License
|
||||
- https://github.com/Anjok07/ultimatevocalremovergui
|
||||
- https://huggingface.co/seanghay/uvr_models/resolve/main/
|
||||
- https://huggingface.co/spaces/r3gm/SoniTranslate_translate_audio_of_a_video_content/tree/main
|
||||
- https://huggingface.co/Blane187/all_public_uvr_models/tree/main
|
||||
- https://huggingface.co/Politrees/UVR_resources
|
||||
- https://github.com/Politrees/UVR_resources/tree/main
|
||||
- https://huggingface.co/spaces/ake178178/Ultimate-Vocal-Remover-WebUI-kb
|
||||
- https://arxiv.org/pdf/2111.12203
|
||||
- https://huggingface.co/set-soft/audio_separation
|
||||
Reference in New Issue
Block a user