From dc4491c2a043fba6145d9664f6fdedce7f7035e7 Mon Sep 17 00:00:00 2001
From: reinerterig <54500223+reinerterig@users.noreply.github.com>
Date: Sun, 3 Sep 2023 05:15:58 +1000
Subject: [PATCH 1/2] Mac mps compatibility

---
 __pycache__/test.cpython-311.pyc           | Bin 0 -> 12217 bytes
 __pycache__/utils.cpython-311.pyc          | Bin 0 -> 1626 bytes
 data/__pycache__/__init__.cpython-311.pyc  | Bin 0 -> 196 bytes
 data/__pycache__/dataset.cpython-311.pyc   | Bin 0 -> 9174 bytes
 data/__pycache__/musdb.cpython-311.pyc     | Bin 0 -> 6727 bytes
 data/__pycache__/utils.cpython-311.pyc     | Bin 0 -> 3492 bytes
 model/Untitled-1.ipynb                     |  78 +++++++++++++++++++++
 model/__pycache__/__init__.cpython-311.pyc | Bin 0 -> 197 bytes
 model/__pycache__/conv.cpython-311.pyc     | Bin 0 -> 3317 bytes
 model/__pycache__/crop.cpython-311.pyc     | Bin 0 -> 981 bytes
 model/__pycache__/resample.cpython-311.pyc | Bin 0 -> 6191 bytes
 model/__pycache__/utils.cpython-311.pyc    | Bin 0 -> 5442 bytes
 model/__pycache__/waveunet.cpython-311.pyc | Bin 0 -> 14178 bytes
 model/utils.py                             |  15 ++--
 model/waveunet.py                          |   8 ++-
 train.py                                   |  28 ++++----
 16 files changed, 108 insertions(+), 21 deletions(-)
 create mode 100644 __pycache__/test.cpython-311.pyc
 create mode 100644 __pycache__/utils.cpython-311.pyc
 create mode 100644 data/__pycache__/__init__.cpython-311.pyc
 create mode 100644 data/__pycache__/dataset.cpython-311.pyc
 create mode 100644 data/__pycache__/musdb.cpython-311.pyc
 create mode 100644 data/__pycache__/utils.cpython-311.pyc
 create mode 100644 model/Untitled-1.ipynb
 create mode 100644 model/__pycache__/__init__.cpython-311.pyc
 create mode 100644 model/__pycache__/conv.cpython-311.pyc
 create mode 100644 model/__pycache__/crop.cpython-311.pyc
 create mode 100644 model/__pycache__/resample.cpython-311.pyc
 create mode 100644 model/__pycache__/utils.cpython-311.pyc
 create mode 100644 model/__pycache__/waveunet.cpython-311.pyc

diff --git a/__pycache__/test.cpython-311.pyc b/__pycache__/test.cpython-311.pyc
new file mode 100644
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diff --git a/model/Untitled-1.ipynb b/model/Untitled-1.ipynb
new file mode 100644
index 0000000..c7c365e
--- /dev/null
+++ b/model/Untitled-1.ipynb
@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([8, 6, 16384])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "\n",
+    "class FourierLayer(nn.Module):\n",
+    "    def __init__(self, B, concat_original=True):\n",
+    "        super(FourierLayer, self).__init__()\n",
+    "        self.B = B\n",
+    "        self.concat_original = concat_original\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Applying the transformation for each value in the channel\n",
+    "        cos_transform = torch.cos(2 * np.pi * self.B * x)\n",
+    "        sin_transform = torch.sin(2 * np.pi * self.B * x)\n",
+    "\n",
+    "        # Stacking the transformed channels\n",
+    "        transformed = torch.stack([cos_transform, sin_transform], dim=1)\n",
+    "\n",
+    "        # Reshaping to match original shape but with added channels\n",
+    "        transformed = transformed.view(x.shape[0], -1, x.shape[2])\n",
+    "\n",
+    "        # Optionally concatenate with the original data\n",
+    "        if self.concat_original:\n",
+    "            return torch.cat([x, transformed], dim=1)\n",
+    "        else:\n",
+    "            return transformed\n",
+    "\n",
+    "# Testing the FourierLayer\n",
+    "dummy_input = torch.randn(8, 2, 16384)\n",
+    "fourier_layer = FourierLayer(B=0.1, concat_original=True)\n",
+    "fourier_output = fourier_layer(dummy_input)\n",
+    "fourier_output.shape\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torchGPU",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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literal 0
HcmV?d00001

diff --git a/model/utils.py b/model/utils.py
index 4c92c95..bc7738b 100644
--- a/model/utils.py
+++ b/model/utils.py
@@ -13,13 +13,13 @@ def save_model(model, optimizer, state, path):
     }, path)
 
 
-def load_model(model, optimizer, path, cuda):
+
+def load_model(model, optimizer, path, device):
     if isinstance(model, torch.nn.DataParallel):
         model = model.module  # load state dict of wrapped module
-    if cuda:
-        checkpoint = torch.load(path)
-    else:
-        checkpoint = torch.load(path, map_location='cpu')
+        
+    checkpoint = torch.load(path, map_location=device)
+
     try:
         model.load_state_dict(checkpoint['model_state_dict'])
     except:
@@ -32,8 +32,13 @@ def load_model(model, optimizer, path, cuda):
                 k = k[len(prefix):]
             model_state_dict_fixed[k] = v
         model.load_state_dict(model_state_dict_fixed)
+
+    # Ensure the model is moved to the correct device
+    model.to(device)
+
     if optimizer is not None:
         optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        
     if 'state' in checkpoint:
         state = checkpoint['state']
     else:
diff --git a/model/waveunet.py b/model/waveunet.py
index a14aa55..2391f5d 100644
--- a/model/waveunet.py
+++ b/model/waveunet.py
@@ -100,9 +100,10 @@ def get_input_size(self, output_size):
         return curr_size
 
 class Waveunet(nn.Module):
-    def __init__(self, num_inputs, num_channels, num_outputs, instruments, kernel_size, target_output_size, conv_type, res, separate=False, depth=1, strides=2):
+    def __init__(self, num_inputs, num_channels, num_outputs, instruments, kernel_size, target_output_size, conv_type, res, separate=False, depth=1, strides=2, device=None):
         super(Waveunet, self).__init__()
 
+        self.device = device if device is not None else torch.device('cpu')
         self.num_levels = len(num_channels)
         self.strides = strides
         self.kernel_size = kernel_size
@@ -111,7 +112,7 @@ def __init__(self, num_inputs, num_channels, num_outputs, instruments, kernel_si
         self.depth = depth
         self.instruments = instruments
         self.separate = separate
-
+        self.to(self.device)
         # Only odd filter kernels allowed
         assert(kernel_size % 2 == 1)
 
@@ -195,9 +196,10 @@ def forward_module(self, x, module):
         :param module: Network module to be used for prediction
         :return: Source estimates
         '''
+        x = x.to(self.device)
         shortcuts = []
         out = x
-
+        #print(x.shape)
         # DOWNSAMPLING BLOCKS
         for block in module.downsampling_blocks:
             out, short = block(out)
diff --git a/train.py b/train.py
index 391d379..3164f6f 100644
--- a/train.py
+++ b/train.py
@@ -22,19 +22,21 @@
 
 def main(args):
     #torch.backends.cudnn.benchmark=True # This makes dilated conv much faster for CuDNN 7.5
-
+    device = "cuda" if args.cuda else "cpu"
+    if args.mps:
+            device = torch.device('mps')
     # MODEL
     num_features = [args.features*i for i in range(1, args.levels+1)] if args.feature_growth == "add" else \
                    [args.features*2**i for i in range(0, args.levels)]
     target_outputs = int(args.output_size * args.sr)
     model = Waveunet(args.channels, num_features, args.channels, args.instruments, kernel_size=args.kernel_size,
-                     target_output_size=target_outputs, depth=args.depth, strides=args.strides,
-                     conv_type=args.conv_type, res=args.res, separate=args.separate)
+                 target_output_size=target_outputs, depth=args.depth, strides=args.strides,
+                 conv_type=args.conv_type, res=args.res, separate=args.separate, device=device).to(device)
 
-    if args.cuda:
+    if args.cuda or args.mps:
         model = model_utils.DataParallel(model)
-        print("move model to gpu")
-        model.cuda()
+        print(f"move model to {device}")
+        model.to(device)
 
     print('model: ', model)
     print('parameter count: ', str(sum(p.numel() for p in model.parameters())))
@@ -75,7 +77,7 @@ def main(args):
     # LOAD MODEL CHECKPOINT IF DESIRED
     if args.load_model is not None:
         print("Continuing training full model from checkpoint " + str(args.load_model))
-        state = model_utils.load_model(model, optimizer, args.load_model, args.cuda)
+        state = model_utils.load_model(model, optimizer, args.load_model, device)
 
     print('TRAINING START')
     while state["worse_epochs"] < args.patience:
@@ -85,10 +87,9 @@ def main(args):
         with tqdm(total=len(train_data) // args.batch_size) as pbar:
             np.random.seed()
             for example_num, (x, targets) in enumerate(dataloader):
-                if args.cuda:
-                    x = x.cuda()
-                    for k in list(targets.keys()):
-                        targets[k] = targets[k].cuda()
+                x = x.to(device)
+                for k in list(targets.keys()):
+                    targets[k] = targets[k].to(device)
 
                 t = time.time()
 
@@ -139,13 +140,12 @@ def main(args):
         print("Saving model...")
         model_utils.save_model(model, optimizer, state, checkpoint_path)
 
-
     #### TESTING ####
     # Test loss
     print("TESTING")
 
     # Load best model based on validation loss
-    state = model_utils.load_model(model, None, state["best_checkpoint"], args.cuda)
+    state = model_utils.load_model(model, None, state["best_checkpoint"], device)
     test_loss = validate(args, model, criterion, test_data)
     print("TEST FINISHED: LOSS: " + str(test_loss))
     writer.add_scalar("test_loss", test_loss, state["step"])
@@ -176,6 +176,8 @@ def main(args):
                         help="List of instruments to separate (default: \"bass drums other vocals\")")
     parser.add_argument('--cuda', action='store_true',
                         help='Use CUDA (default: False)')
+    parser.add_argument('--mps', action='store_true',
+                        help='Use MPS on M1 Mac (default: False)')
     parser.add_argument('--num_workers', type=int, default=1,
                         help='Number of data loader worker threads (default: 1)')
     parser.add_argument('--features', type=int, default=32,

From 5cdd4409f59c16d043d46d382758ec7a0211bfb9 Mon Sep 17 00:00:00 2001
From: reinerterig <54500223+reinerterig@users.noreply.github.com>
Date: Sun, 3 Sep 2023 05:21:07 +1000
Subject: [PATCH 2/2] Delete model/Untitled-1.ipynb

---
 model/Untitled-1.ipynb | 78 ------------------------------------------
 1 file changed, 78 deletions(-)
 delete mode 100644 model/Untitled-1.ipynb

diff --git a/model/Untitled-1.ipynb b/model/Untitled-1.ipynb
deleted file mode 100644
index c7c365e..0000000
--- a/model/Untitled-1.ipynb
+++ /dev/null
@@ -1,78 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([8, 6, 16384])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import numpy as np\n",
-    "\n",
-    "class FourierLayer(nn.Module):\n",
-    "    def __init__(self, B, concat_original=True):\n",
-    "        super(FourierLayer, self).__init__()\n",
-    "        self.B = B\n",
-    "        self.concat_original = concat_original\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        # Applying the transformation for each value in the channel\n",
-    "        cos_transform = torch.cos(2 * np.pi * self.B * x)\n",
-    "        sin_transform = torch.sin(2 * np.pi * self.B * x)\n",
-    "\n",
-    "        # Stacking the transformed channels\n",
-    "        transformed = torch.stack([cos_transform, sin_transform], dim=1)\n",
-    "\n",
-    "        # Reshaping to match original shape but with added channels\n",
-    "        transformed = transformed.view(x.shape[0], -1, x.shape[2])\n",
-    "\n",
-    "        # Optionally concatenate with the original data\n",
-    "        if self.concat_original:\n",
-    "            return torch.cat([x, transformed], dim=1)\n",
-    "        else:\n",
-    "            return transformed\n",
-    "\n",
-    "# Testing the FourierLayer\n",
-    "dummy_input = torch.randn(8, 2, 16384)\n",
-    "fourier_layer = FourierLayer(B=0.1, concat_original=True)\n",
-    "fourier_output = fourier_layer(dummy_input)\n",
-    "fourier_output.shape\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "torchGPU",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}