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Formatted Model Summary (#348)
* Formatted Model Summary Aligns the model summary output using f-string formatting. The logic structure of the base class has not been changed, only the lines put into `model_summary_text`. Output width is calculated from keys & values and will scale to show a clean summary for any model/platform. GPU VRAM has been added as an output. Incorrect detection of VRAM is possible in broken environments and GPUs of different sizes can report the same name. Showing it here adds clarity for the user and for issue tickets. Concatenation changed from "\r\n" to "\n", CRLF end of lines for Windows are handled transparently so using it here caused extra blank lines in the summary txt file. **Examples:** Using CUDA + SAE-LIAE ``` ============= Model Summary ============== == == == Model name: SAE == == == == Current iteration: 16 == == == ==----------- Model Options ------------== == == == batch_size: 4 == == sort_by_yaw: False == == random_flip: True == == resolution: 128 == == face_type: f == == learn_mask: True == == optimizer_mode: 1 == == archi: liae == == ae_dims: 256 == == e_ch_dims: 42 == == d_ch_dims: 21 == == multiscale_decoder: False == == ca_weights: False == == pixel_loss: False == == face_style_power: 0.0 == == bg_style_power: 0.0 == == apply_random_ct: False == == clipgrad: False == == == ==------------- Running On -------------== == == == Device index: 0 == == Name: GeForce GTX 1080 == == VRAM: 8.00GB == == == ========================================== ``` Colab ``` ========== Model Summary ========== == == == Model name: SAE == == == == Current iteration: 39822 == == == ==-------- Model Options --------== == == == batch_size: 24 == == sort_by_yaw: True == == random_flip: False == == resolution: 128 == == face_type: f == == learn_mask: True == == optimizer_mode: 2 == == archi: liae == == ae_dims: 222 == == e_ch_dims: 34 == == d_ch_dims: 16 == == multiscale_decoder: True == == ca_weights: True == == pixel_loss: False == == face_style_power: 2.0 == == bg_style_power: 1.5 == == apply_random_ct: False == == clipgrad: True == == == ==--------- Running On ----------== == == == Device index: 0 == == Name: Tesla K80 == == VRAM: 11.00GB == == == =================================== ``` Using OpenCL + H128 ``` =========================== Model Summary =========================== == == == Model name: H128 == == == == Current iteration: 0 == == == ==------------------------- Model Options -------------------------== == == == batch_size: 4 == == sort_by_yaw: False == == random_flip: True == == lighter_ae: False == == pixel_loss: False == == == ==-------------------------- Running On ---------------------------== == == == Device index: 0 == == Name: Advanced Micro Devices, Inc. gfx900 (OpenCL) == == VRAM: 7.98GB == == == ===================================================================== ``` Using CPU (output trimmed) ``` ==------- Running On --------== == == == Using device: CPU == == == =============================== ``` multi_gpu support is retained (output trimmed) ``` ==------------- Running On -------------== == == == Using multi_gpu: True == == == == Device index: 1 == == Name: Geforce GTX 1080 == == VRAM: 8.00GB == == Device index: 2 == == Name: Geforce GTX 1080 == == VRAM: 8.00GB == == == ========================================== ``` Low VRAM warning (output trimmed) ``` ==------------- Running On -------------== == == == Device index: 0 == == Name: Geforce GTX 1050 == == VRAM: 2.00GB == == == ========================================== /!\ /!\ WARNING: /!\ You are using a GPU with 2GB or less VRAM. This may significantly reduce the quality of your result! /!\ If training does not start, close all programs and try again. /!\ Also you can disable Windows Aero Desktop to increase available VRAM. /!\ ``` * Fix indent
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iperov
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1 changed files with 42 additions and 24 deletions
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@ -231,36 +231,54 @@ class ModelBase(object):
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else:
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self.sample_for_preview = self.generate_next_sample()
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self.last_sample = self.sample_for_preview
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###Generate text summary of model hyperparameters
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#Find the longest key name and value string. Used as column widths.
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width_name = max([len(k) for k in self.options.keys()] + [17]) + 1 # Single space buffer to left edge. Minimum of 17, the length of the longest static string used "Current iteration"
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width_value = max([len(str(x)) for x in self.options.values()] + [len(str(self.iter)), len(self.get_model_name())]) + 1 # Single space buffer to right edge
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if not self.device_config.cpu_only: #Check length of GPU names
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width_value = max([len(nnlib.device.getDeviceName(idx))+1 for idx in self.device_config.gpu_idxs] + [width_value])
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width_total = width_name + width_value + 2 #Plus 2 for ": "
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model_summary_text = []
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model_summary_text += ["===== Model summary ====="]
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model_summary_text += ["== Model name: " + self.get_model_name()]
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model_summary_text += ["=="]
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model_summary_text += ["== Current iteration: " + str(self.iter)]
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model_summary_text += ["=="]
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model_summary_text += ["== Model options:"]
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model_summary_text += [f'=={" Model Summary ":=^{width_total}}=='] # Model/status summary
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={"Model name": >{width_name}}: {self.get_model_name(): <{width_value}}=='] # Name
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={"Current iteration": >{width_name}}: {str(self.iter): <{width_value}}=='] # Iter
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={" Model Options ":-^{width_total}}=='] # Model options
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model_summary_text += [f'=={" "*width_total}==']
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for key in self.options.keys():
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model_summary_text += ["== |== %s : %s" % (key, self.options[key])]
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model_summary_text += [f'=={key: >{width_name}}: {str(self.options[key]): <{width_value}}=='] # self.options key/value pairs
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={" Running On ":-^{width_total}}=='] # Training hardware info
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model_summary_text += [f'=={" "*width_total}==']
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if self.device_config.multi_gpu:
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model_summary_text += ["== |== multi_gpu : True "]
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model_summary_text += ["== Running on:"]
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model_summary_text += [f'=={"Using multi_gpu": >{width_name}}: {"True": <{width_value}}=='] # multi_gpu
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model_summary_text += [f'=={" "*width_total}==']
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if self.device_config.cpu_only:
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model_summary_text += ["== |== [CPU]"]
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model_summary_text += [f'=={"Using device": >{width_name}}: {"CPU": <{width_value}}=='] # cpu_only
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else:
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for idx in self.device_config.gpu_idxs:
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model_summary_text += ["== |== [%d : %s]" % (idx, nnlib.device.getDeviceName(idx))]
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if not self.device_config.cpu_only and self.device_config.gpu_vram_gb[0] == 2:
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model_summary_text += ["=="]
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model_summary_text += ["== WARNING: You are using 2GB GPU. Result quality may be significantly decreased."]
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model_summary_text += ["== If training does not start, close all programs and try again."]
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model_summary_text += ["== Also you can disable Windows Aero Desktop to get extra free VRAM."]
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model_summary_text += ["=="]
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model_summary_text += ["========================="]
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model_summary_text = "\r\n".join (model_summary_text)
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model_summary_text += [f'=={"Device index": >{width_name}}: {idx: <{width_value}}=='] # GPU hardware device index
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model_summary_text += [f'=={"Name": >{width_name}}: {nnlib.device.getDeviceName(idx): <{width_value}}=='] # GPU name
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vram_str = f'{nnlib.device.getDeviceVRAMTotalGb(idx):.2f}GB' # GPU VRAM - Formated as #.## (or ##.##)
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model_summary_text += [f'=={"VRAM": >{width_name}}: {vram_str: <{width_value}}==']
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model_summary_text += [f'=={" "*width_total}==']
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model_summary_text += [f'=={"="*width_total}==']
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if not self.device_config.cpu_only and self.device_config.gpu_vram_gb[0] <= 2: # Low VRAM warning
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model_summary_text += ["/!\\"]
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model_summary_text += ["/!\\ WARNING:"]
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model_summary_text += ["/!\\ You are using a GPU with 2GB or less VRAM. This may significantly reduce the quality of your result!"]
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model_summary_text += ["/!\\ If training does not start, close all programs and try again."]
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model_summary_text += ["/!\\ Also you can disable Windows Aero Desktop to increase available VRAM."]
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model_summary_text += ["/!\\"]
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model_summary_text = "\n".join (model_summary_text)
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self.model_summary_text = model_summary_text
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io.log_info(model_summary_text)
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