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| 'Number: {} -- Actual: {} -- Prediction: {}'.format( | ||
| num, check_fizbuz(num), outli[hyp[i].max(0)[1].item()])) | ||
| f'Number: {num} -- Actual: {check_fizbuz(num)} -- Prediction: {outli[hyp[i].max(0)[1].item()]}' | ||
| ) |
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Lines 80-81 refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
| b2 = torch.zeros(1, output_size, requires_grad=True, device=device, dtype=dtype) | ||
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| no_of_batches = int(len(trX) / batches) | ||
| no_of_batches = len(trX) // batches |
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Lines 35-110 refactored with the following changes:
- Simplify division expressions (
simplify-division) - Replace call to format with f-string (
use-fstring-for-formatting)
| x_ = x[0:10] | ||
| y_ = y[0:10] | ||
| x_ = x[:10] | ||
| y_ = y[:10] |
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Lines 92-93 refactored with the following changes:
- Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] [×2] (
remove-redundant-slice-index)
| x_ = x[0:10] | ||
| y_ = y[0:10] | ||
| x_ = x[:10] | ||
| y_ = y[:10] |
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Lines 94-95 refactored with the following changes:
- Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] [×2] (
remove-redundant-slice-index)
| raise FileNotFoundError("Couldn't find 'labels' folder in {}".format(path)) | ||
| raise FileNotFoundError(f"Couldn't find 'labels' folder in {path}") | ||
| self.files = [] | ||
| for file in input_files: | ||
| name, ext = os.path.splitext(file) | ||
| input_file = os.path.join(inputdir_path, file) | ||
| label_file = os.path.join(labledir_path, '{}_L{}'.format(name, ext)) | ||
| label_file = os.path.join(labledir_path, f'{name}_L{ext}') |
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Function CamvidDataSet.__init__ refactored with the following changes:
- Replace call to format with f-string [×2] (
use-fstring-for-formatting)
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| model.train() | ||
| for epoch in range(epochs): | ||
| for _ in range(epochs): |
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Lines 76-101 refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore) - Remove unnecessary calls to
enumeratewhen the index is not used (remove-unused-enumerate)
| super().__init__() | ||
| self.res_blocks = torch.nn.ModuleList() | ||
| for s in range(stack_size): | ||
| for _ in range(stack_size): |
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Function ResidualStack.__init__ refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
| decoded = np.argmax(data, axis=axis) | ||
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| return decoded | ||
| return np.argmax(data, axis=axis) |
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Function one_hot_decode refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| waveform = np.sign(expanded) * (np.exp(np.abs(expanded) * np.log(mu + 1)) - 1) / mu | ||
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| return waveform | ||
| return np.sign(expanded) * (np.exp(np.abs(expanded) * np.log(mu + 1)) - 1) / mu |
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Function mu_law_decode refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| self.filenames = [x for x in sorted(os.listdir(data_dir))] | ||
| self.filenames = list(sorted(os.listdir(data_dir))) |
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Function Dataset.__init__ refactored with the following changes:
- Replace identity comprehension with call to collection constructor (
identity-comprehension)
| return self.sample_size if len(audio[0]) >= self.sample_size\ | ||
| else len(audio[0]) | ||
| return min(len(audio[0]), self.sample_size) |
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Function DataLoader.calc_sample_size refactored with the following changes:
- Replace comparison with min/max call (
min-max-identity)
| self.files_A = sorted(glob.glob(os.path.join(root, '%sA' % mode) + '/*.*')) | ||
| self.files_B = sorted(glob.glob(os.path.join(root, '%sB' % mode) + '/*.*')) | ||
| self.files_A = sorted(glob.glob(f"{os.path.join(root, f'{mode}A')}/*.*")) | ||
| self.files_B = sorted(glob.glob(f"{os.path.join(root, f'{mode}B')}/*.*")) |
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Function ImageDataset.__init__ refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation) - Replace interpolated string formatting with f-string [×2] (
replace-interpolation-with-fstring)
| elif random.uniform(0, 1) > 0.5: | ||
| i = random.randint(0, self.max_size - 1) | ||
| to_return.append(self.data[i].clone()) | ||
| self.data[i] = element | ||
| else: | ||
| if random.uniform(0, 1) > 0.5: | ||
| i = random.randint(0, self.max_size - 1) | ||
| to_return.append(self.data[i].clone()) | ||
| self.data[i] = element | ||
| else: | ||
| to_return.append(element) | ||
| to_return.append(element) |
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Function ReplayBuffer.push_and_pop refactored with the following changes:
- Merge else clause's nested if statement into elif (
merge-else-if-into-elif)
| for epoch in range(opt.epoch, opt.n_epochs): | ||
| for i, batch in enumerate(dataloader): | ||
| for _ in range(opt.epoch, opt.n_epochs): | ||
| for batch in dataloader: |
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Lines 319-320 refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore) - Remove unnecessary calls to
enumeratewhen the index is not used (remove-unused-enumerate)
| parser.add_argument('--size', type=int, default=256, help='crop to this size') | ||
| args = parser.parse_args(args=[]) | ||
| return args | ||
| return parser.parse_args(args=[]) |
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Function get_args refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| data_names = [] | ||
| data_shapes = [] |
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Function MXNetModelService.initialize refactored with the following changes:
- Move assignment closer to its usage within a block [×2] (
move-assign-in-block) - Replace call to format with f-string (
use-fstring-for-formatting) - Merge append into list declaration [×2] (
merge-list-append)
| data = batch[0].get('body').get(param_name) | ||
| if data: | ||
| if data := batch[0].get('body').get(param_name): | ||
| self.input = data + 1 | ||
| tensor = mx.nd.array([self.binary_encoder(self.input, input_size=10)]) | ||
| return tensor | ||
| return mx.nd.array([self.binary_encoder(self.input, input_size=10)]) |
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Function MXNetModelService.preprocess refactored with the following changes:
- Use named expression to simplify assignment and conditional (
use-named-expression) - Inline variable that is immediately returned (
inline-immediately-returned-variable)
| out = [{'next_number': prediction}] | ||
| return out | ||
| return [{'next_number': prediction}] |
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Function MXNetModelService.postprocess refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| return input_num | ||
| else: | ||
| return input_output_map[prediction] | ||
| return input_num if prediction == 3 else input_output_map[prediction] |
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Function get_readable_output refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
| output_dist = output.squeeze().div(0.8).exp() | ||
| prob = torch.multinomial(output_dist, 2) | ||
| return prob | ||
| return torch.multinomial(output_dist, 2) |
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Function post_processing refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
Branch
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