Update README; NumPy 1.24 forward compatibility; additional linter co…

…mpatibility (#237)

.gitignore

@@ -17,4 +17,5 @@
 **/__pycache__/
 /super-linter.log
 /super-linter.report
-/venv/
+/venv*/
+/typings/

README.md

@@ -1,6 +1,6 @@
 # ADI MAX78000/MAX78002 Model Training and Synthesis
 
-June 14, 2023
+June 27, 2023
 
 ADI’s MAX78000/MAX78002 project is comprised of five repositories:
 
@@ -2681,7 +2681,69 @@ Certain networks share weights between layers. The tools automatically deduplica
 Example:
         `weight_source: conv1p3`
 
-##### Dropout and Batch Normalization
+#### Rolling Buffers
+
+Certain networks (such as TCN) require rolling data buffers. These are implemented in the YAML file (for a complete example, please see `networks/ai85-kinetics-actiontcn.yaml`).
+
+##### `data_buffer` (Global)
+
+The data buffer is allocated and named using the global `data_buffer` configuration key. `processor`,  `dim` (1D or 2D), `channels`, and `offset` must be defined.
+
+Example:
+
+```yaml
+data_buffer:
+  - processors: 0xffffffff00000000
+    dim: 15
+    channels: 32
+    offset: 0x7fc4
+    name: tcn_buffer
+```
+
+##### `buffer_shift` (per layer)
+
+The buffer is shifted `n` places using `buffer_shift: n`. `in_offset` and `in_dim` are required.
+
+Example:
+
+```yaml
+- processors: 0xffffffff00000000
+    output_processors: 0xffffffff00000000
+    in_offset: 0x7FC8
+    in_channels: 32
+    in_dim: 14
+    in_sequences: tcn_buffer
+    out_offset: 0x7fc4
+    operation: passthrough
+    buffer_shift: 1
+    name: buffer_shift
+```
+
+##### `buffer_insert` (per layer)
+
+New data is added using `buffer_insert: n`.
+
+Example:
+
+```yaml
+ - processors: 0xffffffffffffffff
+    output_processors: 0xffffffff00000000
+    in_offset: 0x5000
+    out_offset: 0x7FFC
+    operation: Conv2d
+    in_sequences: res4_out
+    buffer_insert: 1
+    kernel_size: 3x3
+    pad: 0
+    activate: ReLU
+    name: conv5
+```
+
+##### Buffer use
+
+The buffer is used with `in_sequences`, in the example `in_sequences: tcn_buffer`. To use the buffer contents as input, `in_offset` and `in_dim` are required.
+
+#### Dropout and Batch Normalization
 
 * Dropout is only used during training, and corresponding YAML entries are not needed.
 * Batch normalization (“batchnorm”) is fused into the preceding layer’s weights and bias values (see [Batch Normalization](#batch-normalization)), and YAML entries are not needed.

datasets/afsk.py

@@ -44,7 +44,7 @@ def __init__(self, root, train, transform=None):
             with open(os.path.join(self.processed_path, self.train1fn), 'rb') as fd:
                 onebits = np.fromfile(fd, dtype=np.uint8)
         else:
-            raise Exception('Unable to locate training data')
+            raise RuntimeError('Unable to locate training data')
 
         # Make available an equal amount from each classification
         numbitper = int(min([len(zerobits), len(onebits)]) / BYTES_PER_SAMPLE)
@@ -71,10 +71,12 @@ def __len__(self):
         return self.avail
 
     def __getitem__(self, idx):
+        assert self.data is not None and self.avail is not None
+
         # Index [0 avail) to byte offset
         offs = idx * BYTES_PER_SAMPLE
 
-        sampl = self.data[offs:offs + BYTES_PER_SAMPLE].astype(np.float)
+        sampl = self.data[offs:offs + BYTES_PER_SAMPLE].astype(np.float64)
 
         # min-max normalization (rescaling)
         _min = sampl.min()

datasets/aisegment.py

@@ -360,7 +360,7 @@ def __getitem__(self, index):
         if self.transform is not None:
             img = self.transform(img)
 
-        return img, lbl.astype(np.long)
+        return img, lbl.astype(np.int64)
 
 
 def AISegment_get_datasets(data, load_train=True, load_test=True, im_size=(80, 80),

datasets/camvid.py

@@ -74,8 +74,8 @@ def __init__(self, root_dir, d_type, classes=None, download=True, transform=None
             lbl = np.zeros((lbl_rgb.shape[0], lbl_rgb.shape[1]), dtype=np.uint8)
 
             for label_idx, (_, mask) in enumerate(self.label_mask_dict.items()):
-                res = (lbl_rgb == mask)
-                res = (label_idx+1) * res.all(axis=2)
+                res = lbl_rgb == mask
+                res = (label_idx + 1) * res.all(axis=2)
                 lbl += res.astype(np.uint8)
 
             y_start = 0
@@ -153,7 +153,7 @@ def __len__(self):
     def __getitem__(self, idx):
         if self.transform is not None:
             img = self.transform(self.img_list[idx])
-        return img, self.lbl_list[idx].astype(np.long)
+        return img, self.lbl_list[idx].astype(np.int64)
 
 
 def camvid_get_datasets_s80(data, load_train=True, load_test=True, num_classes=33):

datasets/kinetics.py

@@ -388,7 +388,7 @@ def __getitem__(self, index):
 
         (imgs, lab, _) = self.dataset[index]
 
-        start_ind = np.random.randint(low=0, high=(len(imgs)-self.num_frames_model+1))
+        start_ind = np.random.randint(low=0, high=len(imgs) - self.num_frames_model + 1)
         images = imgs[start_ind:start_ind+self.num_frames_model]
 
         transforms_album = []

datasets/msnoise.py

@@ -301,7 +301,7 @@ def __gen_datasets(self, exp_len=16384, row_len=128, overlap_ratio=0,
                             record, fs = librosa.load(record_path, offset=0, sr=None)
                             rec_len = np.size(record)
                             max_start_time = \
-                                ((rec_len / fs - 1) - (rec_len / fs % noise_time_step))
+                                (rec_len / fs - 1) - (rec_len / fs % noise_time_step)
                             for start_time in np.arange(0,
                                                         int((max_start_time+noise_time_step)*fs),
                                                         int(noise_time_step*fs)):

test_qat.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 ###################################################################################################
 #
-# Copyright (C) 2020-2021 Maxim Integrated Products, Inc. All Rights Reserved.
+# Copyright (C) 2020-2023 Maxim Integrated Products, Inc. All Rights Reserved.
 #
 # Maxim Integrated Products, Inc. Default Copyright Notice:
 # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html
@@ -21,7 +21,7 @@ def create_input_data(num_channels):
     '''
     Creates random data
     '''
-    inp = (2.0 * torch.rand(1, num_channels, 8, 8) - 1.0)  # pylint: disable=no-member
+    inp = 2.0 * torch.rand(1, num_channels, 8, 8) - 1.0  # pylint: disable=no-member
     inp_int = torch.clamp(torch.round(128 * inp), min=-128, max=127.)  # pylint: disable=no-member
     inp = inp_int / 128.
 

train.py

@@ -7,6 +7,8 @@
 # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html
 #
 ###################################################################################################
+# pyright: reportMissingModuleSource=false, reportGeneralTypeIssues=false
+# pyright: reportOptionalSubscript=false
 #
 # Portions Copyright (c) 2018 Intel Corporation
 #
@@ -401,6 +403,7 @@ def main():
         args.workers, args.validation_split, args.deterministic,
         args.effective_train_size, args.effective_valid_size, args.effective_test_size,
         test_only=args.evaluate, collate_fn=args.collate_fn, cpu=args.device == 'cpu')
+    assert train_loader is not None and val_loader is not None
 
     if args.sensitivity is not None:
         sensitivities = np.arange(args.sensitivity_range[0], args.sensitivity_range[1],